BMJ Open Diabetes Research & Care最新文献

Introduction: The association between the gut microbiome and incident type 2 diabetes (T2D) is potentially partly mediated through sphingolipids, however these possible mediating mechanisms have not been investigated. We examined whether sphingolipids mediate the association between gut microbiome and T2D, using data from the Healthy Life in an Urban Setting study.

Research design and methods: Participants were of Dutch or South-Asian Surinamese ethnicity, aged 18-70 years, and without T2D at baseline. A case-cohort design (subcohort n=176, cases incident T2D n=36) was used. The exposure was measured by 16S rRNA sequencing (gut microbiome) and mediator by targeted metabolomics (sphingolipids). Dimensionality reduction was achieved by principle component analysis and Shannon diversity. Cox regression and procrustes analyses were used to assess the association between gut microbiome and T2D and sphingolipids and T2D, and between gut microbiome and sphingolipids, respectively. Mediation was tested familywise using mediation analysis with permutation testing and Bonferroni correction.

Results: Our study confirmed associations between gut microbiome and T2D and sphingolipids and T2D. Additionally, we showed that the gut microbiome was associated with sphingolipids. The association between gut microbiome and T2D was partly mediated by a sphingolipid principal component, which represents a dominance of ceramide species over more complex sphingolipids (HR 1.17; 95% CI 1.08 to 1.28; proportional explained 48%), and by Shannon diversity (HR 0.97; 95% CI 0.95 to 0.99; proportional explained 24.8%).

Conclusions: These data suggest that sphingolipids mediate the association between microbiome and T2D risk. Future research is needed to confirm observed findings and elucidate causality on a molecular level.

Introduction: Diabetic retinopathy (DR) is a common vascular complication of diabetes mellitus and a leading cause of vision loss worldwide. Endothelial cell (EC) heterogeneity has been observed in the pathogenesis of DR. Elucidating the underlying mechanisms governing EC heterogeneity may provide novel insights into EC-specific therapies for DR.

Research design and methods: We used the single-cell data from the Gene Expression Omnibus database to explore EC heterogeneity between diabetic retinas and non-diabetic retinas and identify the potential genes involved in DR. CCK-8 assays, EdU assays, transwell assays, and tube formation assays were conducted to determine the role of the identified gene in angiogenic effects.

Results: Our analysis identified three distinct EC subpopulations in retinas and revealed that Mitochondria-localized glutamic acid-rich protein (Mgarp) gene is potentially involved in the pathogenesis of DR. Silencing of Mgarp significantly suppressed the proliferation, migration, and tube formation capacities in retinal endothelial cells.

Conclusions: This study not only offers new insights into transcriptomic heterogeneity and pathological alteration of retinal ECs but also holds the promise to pave the way for antiangiogenic therapy by targeting EC-specific gene.

Introduction: The Environmental Determinants of Islet Autoimmunity (ENDIA) Study is an ongoing Australian prospective cohort study investigating how modifiable prenatal and early-life exposures drive the development of islet autoimmunity and type 1 diabetes (T1D) in children. In this profile, we describe the cohort's parental demographics, maternal and neonatal outcomes and human leukocyte antigen (HLA) genotypes.

Research design and methods: Inclusion criteria were an unborn child, or infant aged less than 6 months, with a first-degree relative (FDR) with T1D. The primary outcome was persistent islet autoimmunity, with children followed until a T1D diagnosis or 10 years of age. Demographic data were collected at enrollment. Lifestyle, clinical and anthropometric data were collected at each visit during pregnancy and clinical pregnancy and birth data were verified against medical case notes. Data were compared between mothers with and without T1D. HLA genotyping was performed on the ENDIA child and all available FDRs.

Results: The final cohort comprised 1473 infants born to 1214 gestational mothers across 1453 pregnancies, with 80% enrolled during pregnancy. The distribution of familial T1D probands was 62% maternal, 28% paternal and 11% sibling. The frequency of high-risk HLA genotypes was highest in T1D probands, followed by ENDIA infants, and lowest among unaffected family members. Mothers with T1D had higher rates of pregnancy complications and perinatal intervention, and larger babies of shorter gestation. Parent demographics were comparable to the Australian population for age, parity and obesity. A greater percentage of ENDIA parents were Australian born, lived in a major city and had higher socioeconomic advantage and education.

Conclusions: This comprehensive profile provides the context for understanding ENDIA's scope, methodology, unique strengths and limitations. Now fully recruited, ENDIA will provide unique insights into the roles of early-life factors in the development of islet autoimmunity and T1D in the Australian environment.

Trial registration number: ACTRN12613000794707.

Introduction: In this systematic review, we investigated the diagnostic accuracy of surrogate measures of insulin secretion based on fasting samples and the oral glucose tolerance test (OGTT). The first phase of insulin secretion was calculated using two gold standard methods; the hyperglycemic clamp (HGC) test and intravenous glucose tolerance test (IVGTT).

Research design and methods: We conducted searches in the PubMed, Cochrane Central, and Web of Science databases, the last of which was conducted at the end of June 2021. Studies were included that measured first-phase insulin secretion in adults using both a gold-standard reference method (either HGC or IVGTT) and one or more surrogate measures from either fasting samples, OGTT or a meal-tolerance test. QUADAS-2, a revised tool for the quality assessment of diagnostic accuracy studies, was used for quality assessment. Random-effects meta-analyses were performed to examine the correlation between first-phase measured with gold standard and surrogate methods.

Results: A total of 33 articles, encompassing 5362 individuals with normal glucose tolerance, pre-diabetes or type 2 diabetes, were included in our systematic review. Homeostatic model assessment (HOMA)-beta and Insulinogenic Index 30 (IGI(30)) were the surrogate measures validated in the largest number of studies (17 and 13, respectively). HOMA-beta's pooled correlation to the reference methods was 0.48 (95% CI 0.40 to 0.56) The pooled correlation of IGI to the reference methods was 0.61 (95% CI 0.54 to 0.68). The surrogate measures with the highest correlation to the reference methods were Kadowaki (0.67 (95% CI 0.61 to 0.73)) and Stumvoll's first-phase secretion (0.65 (95% CI 0.58 to 0.71)), both calculated from an OGTT.

Conclusions: Surrogate measures from the first 30 min of an OGTT capture the first phase of insulin secretion and are a good choice for epidemiological studies. HOMA-beta has a moderate correlation to the reference methods but is not a measure of the first phase specifically.

Prospero registration number: The meta-analysis was registered at PROSPERO (Id: CRD42020169064) before inclusion started.

Introduction: We previously reported predictors of mortality in 1786 adults with diabetes or stress hyperglycemia (glucose>180 mg/dL twice in 24 hours) admitted with COVID-19 from March 2020 to February 2021 to five university hospitals. Here, we examine predictors of readmission.

Research design and methods: Data were collected locally through retrospective reviews of electronic medical records from 1786 adults with diabetes or stress hyperglycemia who had a hemoglobin A1c (HbA1c) test on initial admission with COVID-19 infection or within 3 months prior to initial admission. Data were entered into a Research Electronic Data Capture (REDCap) web-based repository, and de-identified. Descriptive data are shown as mean±SD, per cent (%) or median (IQR). Student's t-test was used for comparing continuous variables with normal distribution and Mann-Whitney U test was used for data not normally distributed. X² test was used for categorical variable.

Results: Of 1502 patients who were alive after initial hospitalization, 19.4% were readmitted; 90.3% within 30 days (median (IQR) 4 (0-14) days). Older age, lower estimated glomerular filtration rate (eGFR), comorbidities, intensive care unit (ICU) admission, mechanical ventilation, diabetic ketoacidosis (DKA), and longer length of stay (LOS) during the initial hospitalization were associated with readmission. Higher HbA1c, glycemic gap, or body mass index (BMI) were not associated with readmission. Mortality during readmission was 8.0% (n=23). Those who died were older than those who survived (74.9±9.5 vs 65.2±14.4 years, p=0.002) and more likely had DKA during the first hospitalization (p<0.001). Shorter LOS during the initial admission was associated with ICU stay during readmission, suggesting that a subset of patients may have been initially discharged prematurely.

Conclusions: Understanding predictors of readmission after initial hospitalization for COVID-19, including older age, lower eGFR, comorbidities, ICU admission, mechanical ventilation, statin use and DKA but not HbA1c, glycemic gap or BMI, can help guide treatment approaches and future research in adults with diabetes.

Introduction: Type 2 diabetes mellitus (T2DM) is associated with dysbiosis in the gut microbiota (MB). Individually, each medication appears to partially correct this. However, there are no studies on the response of the MB to changes in A1c. Therefore, we investigated the MB's response to intensive glycemic control.

Research design and methods: We studied two groups of patients with uncontrolled T2DM, one group with an A1c <9% (18 patients-G1) and another group with an A1c >9% (13 patients-G2), aiming for at least a 1% reduction in A1c. We collected A1c and fecal samples at baseline, 6, and 12 months. G1 achieved an average A1c reduction of 1.1%, while G2 a reduction of 3.13%.

Results: G1's microbiota saw a decrease in Erysipelotrichaceae_UCG_003 and in Mollicutes order (both linked to metabolic syndrome and associated comorbidities). G2, despite having a more significant reduction in A1c, experienced an increase in the proinflammatory bacteria Megasphaera and Acidaminococcus, and only one beneficial genus, Phascolarctobacterium, increased, producer of butyrate.

Conclusion: Despite a notable A1c outcome, G2 could not restore its MB. This seeming resistance to change, leading to a persistent inflammation component found in G2, might be part of the "metabolic memory" in T2DM.

Introduction: To assess the efficacy of a chitosan-based gel (ChitoCare) for the treatment of non-healing diabetic foot ulcers (DFUs).

Research design and methods: Forty-two patients with chronic DFUs were randomized to the ChitoCare or placebo gel for a 10-week treatment period and 4-week follow-up. The primary study end point was the rate of complete wound closure at week 10, presented as relative rate.

Results: Thirty patients completed the 10-week treatment and 28 completed the 4-week follow-up. The ChitoCare arm achieved 16.7% complete wound closure at week 10 vs 4.2% in the placebo arm (p=0.297), 92.0% vs 37.0% median relative reduction in wound surface area from baseline at week 10 (p=0.008), and 4.62-fold higher likelihood of achieving 75% wound closure at week 10 (p=0.012). Based on the results of the Bates-Jensen Wound Assessment Tool, the wound state at week 10 and the relative improvement from the baseline were significantly better (median 20 vs 24 points, p=0.018, and median 29.8% vs 3.6%, p=0.010, respectively).

Conclusions: ChitoCare gel increased the rate of the DFU healing process. Several secondary end points significantly favored ChitoCare gel.

Trial registration number: NCT04178525.

Introduction: The overall aim of this study was to evaluate the implementation of sodium-glucose cotransporter 2 inhibitors (SGLT2i) among patients in tertiary care with type 2 diabetes mellitus (T2DM) and chronic kidney disease (CKD).

Research design and methods: The cross-sectional analysis was based on outpatients in tertiary diabetes care enrolled in the Swiss Diabetes Registry with T2DM and a study visit January 1, 2020-March 31, 2021. Prevalence of CKD was ascertained as an estimated glomerular filtration rate <60 mL/min/1.73 m² and/or persistent albuminuria as defined by Kidney Disease Improving Global Outcomes, and the proportion of patients prescribed SGLT2i was determined. Documented reasons for non-treatment with SGLT2i were extracted by a retrospective review of the medical records.

Results: Of 368 patients with T2DM, 1.1% (n=4) were excluded due to missing data. Of the remaining 364 patients, 47.3% (n=172) had CKD of which 32.6% (n=56) were prescribed SGLT2i. The majority (75%) of these patients were on treatment already in 2018, before the renoprotective effects of SGLT2i were established. Among the 116 patients without SGLT2i, 19.0% had known contraindications, 9.5% stopped treatment due to adverse events, 5.2% had other reasons, and no underlying reason for non-treatment could be identified for 66.4%.

Conclusions: A divergence between recommended standard of care and implementation in daily clinical practice was observed. Although treatment should always consider patient-specific circumstances, the results highlight the need to reinforce current treatment recommendations to ensure patients benefit from the best available care.

Introduction: We designed and implemented a patient-centered, data-driven, holistic care model with evaluation of its impacts on clinical outcomes in patients with young-onset type 2 diabetes (T2D) for which there is a lack of evidence-based practice guidelines.

Research design and methods: In this 3-year Precision Medicine to Redefine Insulin Secretion and Monogenic Diabetes-Randomized Controlled Trial, we evaluate the effects of a multicomponent care model integrating use of information and communication technology (Joint Asia Diabetes Evaluation (JADE) platform), biogenetic markers and patient-reported outcome measures in patients with T2D diagnosed at ≤40 years of age and aged ≤50 years. The JADE-PRISM group received 1 year of specialist-led team-based management using treatment algorithms guided by biogenetic markers (genome-wide single-nucleotide polymorphism arrays, exome-sequencing of 34 monogenic diabetes genes, C-peptide, autoantibodies) to achieve multiple treatment goals (glycated hemoglobin (HbA1c) <6.2%, blood pressure <120/75 mm Hg, low-density lipoprotein-cholesterol <1.2 mmol/L, waist circumference <80 cm (women) or <85 cm (men)) in a diabetes center setting versus usual care (JADE-only). The primary outcome is incidence of all diabetes-related complications.

Results: In 2020-2021, 884 patients (56.6% men, median (IQR) diabetes duration: 7 (3-12) years, current/ex-smokers: 32.5%, body mass index: 28.40±5.77 kg/m², HbA1c: 7.52%±1.66%, insulin-treated: 27.7%) were assigned to JADE-only (n=443) or JADE-PRISM group (n=441). The profiles of the whole group included positive family history (74.7%), general obesity (51.4%), central obesity (79.2%), hypertension (66.7%), dyslipidemia (76.4%), albuminuria (35.4%), estimated glomerular filtration rate <60 mL/min/1.73 m² (4.0%), retinopathy (13.8%), atherosclerotic cardiovascular disease (5.2%), cancer (3.1%), emotional distress (26%-38%) and suboptimal adherence (54%) with 5-item EuroQol for Quality of Life index of 0.88 (0.87-0.96). Overall, 13.7% attained ≥3 metabolic targets defined in secondary outcomes. In the JADE-PRISM group, 4.5% had pathogenic/likely pathogenic variants of monogenic diabetes genes; 5% had autoantibodies and 8.4% had fasting C-peptide <0.2 nmol/L. Other significant events included low/large birth weight (33.4%), childhood obesity (50.7%), mental illness (10.3%) and previous suicide attempts (3.6%). Among the women, 17.3% had polycystic ovary syndrome, 44.8% required insulin treatment during pregnancy and 17.3% experienced adverse pregnancy outcomes.

Conclusions: Young-onset diabetes is characterized by complex etiologies with comorbidities including mental illness and lifecourse events.

Trial registration number: NCT04049149.

Introduction: Oxidative stress is known to affect left ventricular functions negatively. There is a strong bidirectional connection between diabetes mellitus (DM) and oxidative stress. In parallel, left ventricular dysfunction is observed more frequently, even in patients with DM without other risk factors. In this context, the objective of this study is to comparatively investigate the potential relationship between oxidative stress and subclinical left ventricular dysfunction (SCLVD) assessed by Myocardial Performance Index (MPI) in patients with and without DM.

Research design and methods: The sample of this observational cross-sectional single-center study consisted of 151 patients who were evaluated for oxidative stress and SCLVD by tissue Doppler echocardiography. Patients' total oxidant status (TOS), total antioxidant status (TAS), and Oxidative Stress Index (OSI) values were calculated. The effects of oxidative stress and DM on MPI were analyzed.

Results: There were 81 patients with DM (mean age: 46.17±10.33 years) and 70 healthy individuals (mean age: 45.72±9.04 years). Mean TOS and OSI values of the DM group were higher than healthy individuals (5.72±0.55 vs 5.31±0.50, p = <0.001; and 4.92±1.93 vs 1.79±0.39, p = <0.001; respectively). The mean TAS value of the DM group was significantly lower than the healthy group (1.21±0.40 vs 3.23±0.51, p = <0.001). There was a significant correlation between OSI and MPI mitral in the DM group (R 0.554, p = <0.001) but not in the healthy group (R -0.069, p=0.249).

Conclusions: Both oxidative stress and myocardial dysfunction were found to be more common in patients with DM. The study's findings indicated the negative effect of oxidative stress on myocardial functions. Accordingly, increased oxidative stress caused more significant deterioration in MPI in patients with DM compared with healthy individuals.