Diabetes care最新文献

Objective: The coronavirus 2019 (COVID-19) pandemic has evolved over time by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variant, disease severity, treatment, and prevention. There is evidence of an elevated risk of incident diabetes after COVID-19; our objective was to evaluate whether this association is consistent across time and with contemporary viral variants.

Research design and methods: We conducted a retrospective cohort study using National COVID Cohort Collaborative (N3C) data to evaluate incident diabetes risk among COVID-positive adults compared with COVID-negative patients or control patients with acute respiratory illness (ARI). Cohorts were weighted on demographics, data site, and Charlson comorbidity index score. The primary outcome was the cumulative incidence ratio (CIR) of incident diabetes for each viral variant era.

Results: Risk of incident diabetes 1 year after COVID-19 was increased for patients with any viral variant compared with COVID-negative control patients (ancestral CIR 1.16 [95% CI 1.12-1.21]; Alpha CIR 1.14 [95% CI 1.11-1.17]; Delta CIR 1.17 [95% CI 1.13-1.21]; Omicron CIR 1.13 [95% CI 1.10-1.17]) and control patients with ARI (ancestral CIR 1.17 [95% CI 1.11-1.22]; Alpha CIR 1.14 [95% CI 1.09-1.19]; Delta CIR 1.18 [95% CI 1.11-1.26]; Omicron CIR 1.20 [95% CI 1.13-1.27]). There was latency in the timing of incident diabetes risk with the Omicron variant; in contrast with other variants, the risk presented after 180 days.

Conclusions: Incident diabetes risk after COVID-19 was similar across different SARS-CoV-2 variants. However, there was greater latency in diabetes onset in the Omicron variant era.

Objective: We aimed to assess the feasibility, clinical accuracy, and acceptance of a hospital-wide continuous glucose monitoring (CGM) policy with electronic health record (EHR)-integrated validation for insulin dosing.

Research design and methods: A hospital policy was developed and implemented at Stanford Health Care for using personal CGMs in lieu of fingerstick blood glucose (FSBG) monitoring. It included requirements specific to each CGM, accuracy monitoring protocols, and EHR integration. User experience surveys were conducted among a subset of patients and nurses.

Results: From November 2022 to August 2023, 135 patients used the CGM protocol in 185 inpatient encounters. This group included 27% with type 1 diabetes and 24% with automated insulin delivery systems. The most-used CGMs were Dexcom G6 (44%) and FreeStyle Libre 2 (43%). Of 1,506 CGM validation attempts, 87.8% met the 20% or 20 mg/dL (%20/20) criterion for CGM-based insulin dosing and 99.3% fell within Clarke zones A or B. User experience surveys were completed by 27 nurses and 46 patients. Most nurses found glucose management under the protocol effective (74%), easy to use (67%), and efficient (63%); 80% of nurses preferred inpatient CGM to FSBG. Most patients liked the CGM protocol (63%), reported positive CGM interactions with nursing staff (63%), and felt no significant interruptions to their diabetes management (63%).

Conclusions: Implementation of a hospital-wide inpatient CGM policy supporting multiple CGM types with real-time accuracy monitoring and integration into the EHR is feasible. Initial feedback from nurses and patients was favorable, and further investigation toward broader use and sustainability is needed.

Objective: Continuous glucose monitoring (CGM) combined with continuous subcutaneous insulin infusion (CSII) achieves better glycemic control than multi-injection therapy in people with type 2 diabetes. The effectiveness of closed-loop therapy needs to be further evaluated in this population.

Research design and methods: The study objective was to measure the impact of a hybrid closed-loop device (DBLG1) compared with CSII + CGM on glycemic control in people with type 2 diabetes previously treated with CSII. The randomized, controlled, crossover, two-period, open-label, and multicenter study was conducted from August 2022 to July 2023 in 17 individuals (9 to receive 6 weeks of CSII + CGM first and 8 to receive 6 weeks of closed-loop therapy first). The primary end point was the percentage time in range (TIR: 70-180 mg/dL). Secondary outcomes were other CGM-glucose metrics, physical activity, and sleep objectively measured using 1-week actimetry.

Results: Data were analyzed using a modified intention-to-treat approach. Mean age was 63 (SD 9) years and 35% were women. Mean HbA1c at inclusion was 7.9% (SD 0.9). TIR increased to 76.0% (interquartile range 69.0-84.0) during the closed-loop condition vs. 61.0% (interquartile range 55.0-70.0) during the CSII + CGM condition; mean difference was 15.0 percentage points (interquartile range 8.0-22.0; P < 0.001). Analyses of secondary end points showed a decrease in time above range, in glucose management indicator, in glucose variability, and an increase in daily insulin dose. Actimetric sleep analysis showed an improvement in sleep fragmentation during closed-loop treatment.

Conclusions: Closed-loop therapy improved glycemic control more than did CSII + CGM in people with type 2 diabetes.

Objective: Diabetes is a well-known risk factor for dementia. We investigated the association between (pre)diabetes and older brain age and whether this can be attenuated by modifiable lifestyle behaviors.

Research design and methods: The study included 31,229 dementia-free adults from the UK Biobank between the ages of 40 and 70 years. Glycemic status (normoglycemia, prediabetes, or diabetes) was ascertained based on medical history, medication use, and HbA1c measured at baseline. Information on cardiometabolic risk factors (obesity, hypertension, low HDL, and high triglycerides) and lifestyle behaviors (smoking, drinking, and physical activity) was also collected at baseline. Participants underwent up to two brain MRI scans over 11 years of follow-up. Brain age was estimated using a machine learning model based on 1,079 brain MRI phenotypes and used to calculate brain age gap (BAG; i.e., brain age minus chronological age).

Results: At baseline, 13,518 participants (43.3%) had prediabetes and 1,149 (3.7%) had diabetes. Prediabetes (β = 0.22 [95% CI 0.10, 0.34]) and diabetes (2.01 [1.70, 2.32]) were both associated with significantly higher BAG, and diabetes was further associated with significant increase in BAG over time (0.27 [0.01, 0.53]). The association between (pre)diabetes and higher BAG was more pronounced in men and in people with two or more cardiometabolic risk factors. In joint exposure analysis, having a healthy lifestyle (i.e., no smoking, no heavy drinking, and high physical activity) significantly attenuated the diabetes-BAG association.

Conclusions: Diabetes and even prediabetes are associated with accelerated brain aging, especially among men and people with poor cardiometabolic health. However, a healthy lifestyle may counteract this.

Objective: To evaluate whether associations between sitting time and all-cause and heart disease mortality are modified by physical activity in adults with diabetes.

Research design and methods: Data came from 6,335 U.S. adults with diabetes from National Health and Nutrition Examination Survey 2007-2018 (baseline), with mortality follow-up through 2019. Sitting time and moderate to vigorous physical activity (MVPA) were self-reported. Cox models were adjusted for sociodemographics, lifestyle factors, and medical conditions.

Results: Over a median follow-up of 5.9 years, 1,278 all-cause and 354 heart disease deaths were documented (mean age, 59.6 years; 48.3% female). Longer sitting time was associated with greater all-cause and heart disease mortality risk in inactive (MVPA <10 min/week) or insufficiently active (MVPA 10 to <150 min/week) adults with diabetes, but not in active adults (MVPA ≥150 min/week) (all-cause mortality: P = 0.003 for interaction; heart disease mortality: P = 0.008 for interaction).

Conclusions: In adults with diabetes, meeting guideline-recommended physical activity may offset the elevated all-cause and heart disease mortality risk associated with excessive sitting time.

Background: The increased risk of pregnancy complications in type 1 diabetes is mainly attributed to maternal hyperglycemia. However, it is unclear whether other potentially modifiable factors also contribute to risk in this population.

Purpose: We sought to assess whether high BMI and excessive gestational weight gain (GWG) are associated with perinatal complications in type 1 diabetes.

Data sources: We searched Medline, Embase, PubMed, Scopus, Web of Science, and Cochrane databases to January 2024.

Study selection: Studies examining associations between periconception BMI or GWG and perinatal complications in type 1 diabetes were included.

Data extraction: We used a predesigned data extraction template to extract study data including year, country, sample size, participants' characteristics, exposure, and outcomes.

Data synthesis: We included 29 studies (18,965 pregnancies; 1978-2019) in the meta-analysis. A 1 kg/m2/1 kg increase in preconception BMI or GWG was associated with a 3% and 11% increase, respectively, in perinatal complications (BMI odds ratio [OR] 1.03 [95% CI 1.01-1.06]; GWG OR 1.11 [95% CI 1.04-1.18]). Preconception BMI ≥ 25 kg/m2 or excessive GWG was associated with a 22% and 50% increase, respectively, in perinatal complications (BMI OR 1.22 [95% CI 1.11-1.34]; GWG OR 1.50 [95% CI 1.31-1.73]). BMI was associated with congenital malformation, preeclampsia, and neonatal intensive care unit admission. Excessive GWG was associated with preeclampsia, cesarean delivery, large for gestational age, and macrosomia.

Limitations: Limitations included retrospective study design, variable measurement for exposures and outcomes, small number of studies for some outcomes, and no data from Asia and Africa.

Conclusions: Addressing maternal BMI prepregnancy and preventing excessive GWG should be key clinical priorities to improve outcomes in pregnant women with type 1 diabetes.

Objective: Type 2 diabetes and glucose metabolism have previously been linked to Alzheimer disease (AD). Yet, findings on the relation of glucose metabolism with amyloid-β and tau pathology later in life remain unclear.

Research design and methods: We included 288 participants (mean age 43.1 years, SD 10.7, range 20-70 years) without dementia, from the Framingham Heart Study, who had available measures of glucose metabolism (i.e., one-time fasting plasma glucose and insulin) and positron emission tomography (PET) measures of amyloid-β and/or tau 14 years later. We performed linear regression analyses to test associations of plasma glucose (continuously and categorically; elevated defined as >100 mg/dL), plasma insulin, homeostatic model assessment for insulin resistance (HOMA-IR) with amyloid-β or tau load on PET. When significant, we explored whether age, sex, and APOE ε4 allele carriership (AD genetic risk) modified these associations.

Results: Our findings indicated that elevated plasma glucose was associated with greater tau load 14 years later (B [95% CI] = 0.03 [0.01-0.05], P = 0.024 after false discovery rate [FDR] correction) but not amyloid-β. APOE ε4 carriership modified this association (B [95% CI] = -0.08 [-0.12 to -0.03], P = 0.001), indicating that the association was only present in APOE ε4 noncarriers (n = 225). Plasma insulin and HOMA-IR were not associated with amyloid-β or tau load 14 years later after FDR correction.

Conclusions: Our findings suggest that glucose metabolism is associated with increased future tau but not amyloid-β load. This provides relevant knowledge for prevention strategies and prognostics to improve health care.