Diabetes Care最新文献

Objective: Identify the improvement in diabetes performance measures and population-based clinical outcomes resulting from changes in care management processes (CMP) in primary care practices over 3 years.

Research design and methods: This repeated cross-sectional study tracked clinical performance measures for all diabetes patients seen in a cohort of 330 primary care practices in 2017 and 2019. Unit of analysis was patient-year with practice-level CMP exposures. Causal inference is based on dynamic changes in individual CMPs between years by practice. We used the Bayesian method to simultaneously estimate a five-outcome model: A1c, systolic and diastolic blood pressure, guideline-based statin use, and Optimal Diabetes Care (ODC). We control for unobserved time-invariant practice characteristics and secular change. We modeled correlation of errors across outcomes. Statistical significance was identified using 99% Bayesian credible intervals (analogous to P < 0.01).

Results: Implementation of 18 of 62 CMPs was associated with statistically significant improvements in patient outcomes. Together, these resulted in 12.1% more patients meeting ODC performance measures. Different CMPs affected different outcomes. Three CMPs accounted for 47% of the total ODC improvement, 68% of A1c decrease, 21% of SBP reduction, and 55% of statin use increase: 1) systems for identifying and reminding patients due for testing, 2) after-visit follow-up by a nonclinician, and 3) guideline-based clinician reminders for preventive services during a clinic visit.

Conclusions: Effective quality improvement in primary care focuses on practice redesign that clearly improves diabetes outcomes. Tailoring CMP adoption in primary care provides effective improvement in ODC performance through focused changes in diabetes outcomes.

Objective: To investigate the causal association of type 2 diabetes and its components with risk of vascular complications independent of shared risk factors obesity and hypertension and to identify the main driver of this risk.

Research design and methods: We conducted Mendelian randomization (MR) using independent genetic variants previously associated with type 2 diabetes, fasting glucose, HbA1c, fasting insulin, BMI, and systolic blood pressure as instrumental variables. We obtained summary-level data for 18 vascular diseases (15 for type 2 diabetes) from FinnGen and publicly available genome-wide association studies as our outcomes. We conducted univariable and multivariable MR, in addition to sensitivity tests to detect and minimize pleiotropic effects.

Results: Univariable MR analysis showed that type 2 diabetes was associated with 9 of 15 outcomes; BMI and systolic blood pressure were associated with 13 and 15 of 18 vascular outcomes, respectively; and fasting insulin was associated with 4 and fasting glucose with 2. No robust association was found for HbA1c instruments. With adjustment for correlated traits in the multivariable test, BMI and systolic blood pressure, consistent causal effects were maintained, while five associations with type 2 diabetes (chronic kidney disease, ischemic heart disease, heart failure, subarachnoid hemorrhage, and intracerebral hemorrhage) were attenuated to null.

Conclusions: Our findings add strong evidence to support the importance of BMI and systolic blood pressure in the development of vascular complications in people with type 2 diabetes. Such findings strongly support the need for better weight and blood pressure management in type 2 diabetes, independent of glucose lowering, to limit important complications.

Objective: In November 2022, teplizumab-mzwv became the first drug approved to delay the onset of stage 3 type 1 diabetes in adults and children age ≥8 years with stage 2 type 1 diabetes on the basis of data from the pivotal study TN-10.

Research design and methods: To provide confirmatory evidence of the effects of teplizumab on preserving endogenous insulin production, an integrated analysis of C-peptide data from 609 patients (n = 375 patients receiving teplizumab and n = 234 control patients) from five clinical trials in stage 3 type 1 diabetes was conducted.

Results: The primary outcome of the integrated analysis, change from baseline in stimulated C-peptide, was significantly improved at years 1 (average increase 0.08 nmol/L; P < 0.0001) and 2 (average increase 0.12 nmol/L; P < 0.0001) after one or two courses of teplizumab. An analysis of exogenous insulin use was also conducted, showing overall reductions of 0.08 (P = 0.0001) and 0.10 units/kg/day (P < 0.0001) at years 1 and 2, respectively. An integrated safety analysis of five clinical trials that enrolled 1,018 patients with stage 2 or 3 type 1 diabetes (∼1,500 patient-years of follow-up for teplizumab-treated patients) was conducted.

Conclusions: These data confirm consistency in the preservation of β-cell function, as measured by C-peptide, across multiple clinical trials. This analysis showed that the most common adverse events included lymphopenia, rash, and headache, a majority of which occurred during and after the first few weeks of teplizumab administration and generally resolved without intervention, consistent with a safety profile characterized by self-limited adverse events after one or two courses of teplizumab treatment.

Background: Numerous laboratory tests are used in the diagnosis and management of patients with diabetes mellitus. The quality of the scientific evidence supporting the use of these assays varies substantially. An expert committee compiled evidence-based recommendations for laboratory analysis in patients with diabetes. The overall quality of the evidence and the strength of the recommendations were evaluated. The draft consensus recommendations were evaluated by invited reviewers and presented for public comment. Suggestions were incorporated as deemed appropriate by the authors (see Acknowledgments in the full version of the guideline). The guidelines were reviewed by the Evidence Based Laboratory Medicine Committee and the Board of Directors of the American Association for Clinical Chemistry and by the Professional Practice Committee of the American Diabetes Association.

Content: Diabetes can be diagnosed by demonstrating increased concentrations of glucose in venous plasma or increased hemoglobin A1c (HbA1c) in the blood. Glycemic control is monitored by the patients measuring their own blood glucose with meters and/or with continuous interstitial glucose monitoring devices and also by laboratory analysis of HbA1c. The potential roles of noninvasive glucose monitoring; genetic testing; and measurement of ketones, autoantibodies, urine albumin, insulin, proinsulin, and C-peptide are addressed.

Summary: The guidelines provide specific recommendations based on published data or derived from expert consensus. Several analytes are found to have minimal clinical value at the present time, and measurement of them is not recommended.

Objective: To study the interaction among HLA genotype, early probiotic exposure, and timing of complementary foods in relation to risk of islet autoimmunity (IA).

Research design and methods: The Environmental Determinants of Diabetes in the Young (TEDDY) study prospectively follows 8,676 children with increased genetic risk of type 1 diabetes. We used a Cox proportional hazards regression model adjusting for potential confounders to study early feeding and the risk of IA in a sample of 7,770 children.

Results: Any solid food introduced early (<6 months) was associated with increased risk of IA if the child had the HLA DR3/4 genotype and no probiotic exposure during the 1st year of life. Rice introduced at 4-5.9 months compared with later in the U.S. was associated with an increased risk of IA.

Conclusions: Timing of solid food introduction, including rice, may be associated with IA in children with the HLA DR3/4 genotype not exposed to probiotics. The microbiome composition under these exposure combinations requires further study.

Objective: We sought to determine real-world accuracy of inpatient continuous glucose monitoring (CGM) at multiple levels of acuity in a large safety-net hospital.

Research design and methods: We analyzed records from hospitalized patients on Dexcom G6 CGM, including clinical, point of care (POC), and laboratory (Lab) glucose, and CGM data. POC/Lab values were matched to the closest timed CGM value. Encounters were divided into not critically ill (NCI) versus critically ill (CI). CGM accuracy was evaluated.

Results: Paired readings (2,744 POC-CGM; 3,705 Lab-CGM) were analyzed for 233 patients with 239 encounters (83 NCI, 156 CI). POC-CGM aggregated and average mean absolute relative differences (MARD) were 15.1% and 17.1%. Lab-CGM aggregated and average MARDs were 11.4% and 12.2%. Accuracy for POC-CGM and Lab-CGM was 96.5% and 99.1% in Clarke Error Grid zones A/B.

Conclusions: Real-world accuracy of inpatient CGM is acceptable for NCI and CI patients. Further exploration of conditions associated with lower CGM accuracy in real-world settings is warranted.

Objective: We explored longitudinal changes associated with switching to hybrid closed-loop (HCL) insulin delivery systems in adults with type 1 diabetes and elevated HbA1c levels despite the use of intermittently scanned continuous glucose monitoring (isCGM) and insulin pump therapy.

Research design and methods: We undertook a pragmatic, preplanned observational study of participants included in the National Health Service England closed-loop pilot. Adults using isCGM and insulin pump across 31 diabetes centers in England with an HbA1c ≥8.5% who were willing to commence HCL therapy were included. Outcomes included change in HbA1c, sensor glucometrics, diabetes distress score, Gold score (hypoglycemia awareness), acute event rates, and user opinion of HCL.

Results: In total, 570 HCL users were included (median age 40 [IQR 29-50] years, 67% female, and 85% White). Mean baseline HbA1c was 9.4 ± 0.9% (78.9 ± 9.1 mmol/mol) with a median follow-up of 5.1 (IQR 3.9-6.6) months. Of 520 users continuing HCL at follow-up, mean adjusted HbA1c reduced by 1.7% (95% CI 1.5, 1.8; P < 0.0001) (18.1 mmol/mol [95% CI 16.6, 19.6]; P < 0.0001). Time in range (70-180 mg/dL) increased from 34.2 to 61.9% (P < 0.001). Individuals with HbA1c of ≤58 mmol/mol rose from 0 to 39.4% (P < 0.0001), and those achieving ≥70% glucose time in range and <4% time below range increased from 0.8 to 28.2% (P < 0.0001). Almost all participants rated HCL therapy as having a positive impact on quality of life (94.7% [540 of 570]).

Conclusions: Use of HCL is associated with improvements in HbA1c, time in range, hypoglycemia, and diabetes-related distress and quality of life in people with type 1 diabetes in the real world.

Objective: We examined whether intake of methyl donor nutrients, including vitamins B2, B6, and B12 and folate, from foods and/or supplements is associated with type 2 diabetes risk.

Research design and methods: We included 203,644 women and men from the Nurses' Health Study (1984-2016), Nurses' Health Study 2 (1991-2017), and Health Professionals Follow-Up Study (1986-2016). Dietary data were collected every 2-4 years with use of semiquantitative food-frequency questionnaires. Cox proportional hazards models with time-varying covariates were used to evaluate associations between each nutrient and type 2 diabetes risk. We combined cohort-specific hazard ratios (HRs) using inverse variance-weighted fixed-effects meta-analyses.

Results: During 4,900,181 person-years of follow-up, we documented 19,475 incident type 2 diabetes cases. In multivariable-adjusted meta-analyses, participants in the highest quintiles of total vitamin B2 and B6 intakes had lower risk of diabetes compared with those in the lowest quintiles (HR 0.93 [95% CI 0.89, 0.98] for B2 and 0.93 [0.89, 0.97] for B6). With stratification by source, significant associations remained for B2 from food but not from supplements. Neither association for B6 from food nor association for B6 from supplements attained significance. No association was observed between total B12 intake and diabetes. However, B12 from food was marginally associated with higher diabetes risk (1.05 [1.00-1.11]) but not after additional adjustment for red meat intake (1.04 [0.99-1.10]). No evidence of association was observed between intakes of folate and diabetes.

Conclusions: The results of our study suggest that higher intake of vitamin B2 and B6, especially B2 from food sources, may be associated with a modestly lower type 2 diabetes risk.

Objective: To develop a risk assessment tool to identify patients with type 2 diabetes (T2D) at higher risk for kidney disease progression and who might benefit more from sodium-glucose cotransporter 2 (SGLT2) inhibition.

Research design and methods: A total of 41,204 patients with T2D from four Thrombolysis In Myocardial Infarction (TIMI) clinical trials were divided into derivation (70%) and validation cohorts (30%). Candidate predictors of kidney disease progression (composite of sustained ≥40% decline in estimated glomerular filtration rate [eGFR], end-stage kidney disease, or kidney death) were selected with multivariable Cox regression. Efficacy of dapagliflozin was assessed by risk categories (low: <0.5%; intermediate: 0.5 to <2%; high: ≥2%) in Dapagliflozin Effect on Cardiovascular Events (DECLARE)-TIMI 58.

Results: There were 695 events over a median follow-up of 2.4 years. The final model comprised eight independent predictors of kidney disease progression: atherosclerotic cardiovascular disease, heart failure, systolic blood pressure, T2D duration, glycated hemoglobin, eGFR, urine albumin-to-creatinine ratio, and hemoglobin. The c-indices were 0.798 (95% CI, 0.774-0.821) and 0.798 (95% CI, 0.765-0.831) in the derivation and validation cohort, respectively. The calibration plot slope (deciles of predicted vs. observed risk) was 0.98 (95% CI, 0.93-1.04) in the validation cohort. Whereas relative risk reductions with dapagliflozin did not differ across risk categories, there was greater absolute risk reduction in patients with higher baseline risk, with a 3.5% absolute risk reduction in kidney disease progression at 4 years in the highest risk group (≥1%/year). Results were similar with the 2022 Chronic Kidney Disease Prognosis Consortium risk prediction model.

Conclusions: Risk models for kidney disease progression can be applied in patients with T2D to stratify risk and identify those who experience a greater magnitude of benefit from SGLT2 inhibition.