Acta Diabetologica最新文献

Aims

The impact of macrovascular and microvascular complications, the common vascular complications of type 2 diabetes, on long-term mortality has been well evaluated, but the impact of different complications of newly diagnosed type 2 diabetes (diagnosed within the past 2 years) on long-term mortality has not been reported. We aimed to investigate the relationship between all-cause mortality and vascular complications in U.S. adults (aged ≥ 20 years) with newly diagnosed type 2 diabetes.

Methods

We used data from the 1999–2018 National Health and Nutritional Examination Surveys (NHANES). Cox proportional hazard models was used to assess hazard ratios (HR) and 95% confidence intervals for all-cause mortality.

Results

A total of 928 participants were enrolled in this study. At a mean follow-up of 10.8 years, 181 individuals died. In the fully adjusted model, the hazard ratio (HR) (95% confidence interval [CI]) of all-cause mortality for individuals with any single complication compared with those with newly diagnosed type 2 diabetes without complications was 2.24 (1.37, 3.69), and for individuals with two or more complications was 5.34 (3.01, 9.46).Co-existing Chronic kidney disease (CKD) and diabetic retinopathy (DR) at baseline were associated with the highest risk of death (HR 6.07[2.92–12.62]), followed by CKD and cardiovascular disease (CVD) (HR 4.98[2.79–8.89]) and CVD and DR (HR 4.58 [1.98–10.57]).

Conclusion

The presence of single and combined diabetes complications exerts a long-term synergistic adverse impact on overall mortality in newly diagnosed U.S. adults with type 2 diabetes, underscoring the importance of comprehensive complication screening to enhance risk stratification and treatment.

Aims

The relationship between frailty and mortality among individuals with varying diabetic statuses represents a burgeoning area of concern and scholarly interest within the medical community. However, there are limited studies that explore the relationship between frailty and mortality, as well as cause-specific mortality among individuals with non-diabetes, prediabetes, and diabetes patients. Hence, this study aims to investigate the relationship between the frailty statues and all-cause mortality, as well as cause-specific mortality in individuals with varying diabetic statuses using the data in the NHANES database.

Methods

The study utilized data from the National Health and Nutrition Examination Survey (NHANES) 1999–2018, incorporating a final sample size of 57, 098 participants. Both univariable and multivariable-adjusted logistic regression analyses, as well as Cox regression analysis were employed to examine the relationship between frailty index (FI) and mortality.

Results

This study, found a significant positive correlation between the frailty and the increased risk of all-cause mortality non-diabetic [OR 4.277, 95%CI (3.982, 4.594), P < 0.001], prediabetic [OR 2.312, 95%CI (2.133, 2.506), P < 0.001], and diabetic patients [OR 3.947, 95%CI (3.378, 4.611), P < 0.001]. This correlation still existed even after adjusting for confounding factors including age, sex, BMI, poverty, fasting insulin, education, smoke, alcohol drink, waist, hypertension, hyperlipidemia, fasting glucose, HbA1c, eGFR, creatinine and total bilirubin. Our result also suggested a significant positive correlation between the frailty index and the increased risk of CVD mortality among non-diabetic [OR 3.095, 95%CI (2.858, 3.352), P < 0.001] and prediabetic [OR 5.985, 95%CI (5.188, 6.904), P < 0.001] individuals. However, in patients with diabetes, the correlation between frailty and CVD mortality lost significance after adjusting for possible confounding factors [OR 1.139, 95%CI (0.794, 1.634), P > 0.05].

Conclusion

A nonlinear relationship has been identified between the FI and all-cause mortality, as well as CVD mortality in non-diabetic and pre-diabetic population. In diabetic patients, there was a significant positive correlation between the frailty and the increased risk of all-cause mortality, but not with CVD mortality. Renal function and liver function might potentially acted as an intermediary factor that elevated the risk of CVD mortality in frail patients with diabetes.

Aims: Diabetic retinopathy (DR) results from complex genetic and metabolic interactions. Unraveling the links between blood metabolites and DR can advance risk prediction and therapy.

Methods: Leveraging Mendelian Randomization (MR) and Linkage Disequilibrium Score Regression (LDSC), we analyzed 10,413 DR cases and 308,633 controls. Data was sourced from the Metabolomics GWAS server and the FinnGen project.

Results: Our research conducted a comprehensive MR analysis across 486 serum metabolites to investigate their causal role in DR. After stringent selection and validation of instrumental variables, we focused on 480 metabolites for analysis. Our findings revealed 38 metabolites potentially causally associated with DR. Specifically, 4-androsten-3beta,17beta-diol disulfate 2 was identified as significantly associated with a reduced risk of DR (OR = 0.471, 95% CI = 0.324-0.684, p = 7.87 × 10^- 5), even after rigorous adjustments for multiple testing. Sensitivity analyses further validated the robustness of this association, and linkage disequilibrium score regression analyses showed no significant genetic correlation between this metabolite and DR, suggesting a specific protective effect against DR.

Conclusions: Our study identifies 4-androsten-3beta,17beta-diol disulfate 2, a metabolite of androgens, as a significant protective factor against diabetic retinopathy, suggesting androgens as potential therapeutic targets.

Aims: Diabetic Retinopathy (DR) is a significant cause of vision loss in diabetic patients, making early detection and accurate severity classification essential for effective management and prevention. This study aims to develop an enhanced DR severity classification approach using advanced model architectures and transfer learning to improve diagnostic accuracy and support better patient care.

Methods: We propose a novel model, Xception Squeeze-and-Excitation Sparse Lightweight Multi-Level Attention U-Net (XceSE_SparseLwMLA-UNet), designed to classify DR severity using fundus images from the Messidor 1 and Messidor 2 datasets. The XceSE_SparseLwMLA-UNet integrates several advanced mechanisms: the Squeeze-and-Excitation (SE) mechanism for adaptive feature recalibration, the Sparse Lightweight Multi-Level Attention (SparseLwMLA) mechanism for effective contextual information integration, and transfer learning from the Xception architecture to enhance feature extraction capabilities. The SE mechanism refines channel-wise feature responses, while SparseLwMLA enhances the model's ability to identify complex DR patterns. Transfer learning utilizes pre-trained weights from Xception to improve generalization across DR severity levels.

Results: The proposed XceSE_SparseLwMLA-UNet model demonstrates superior performance in DR severity classification, achieving higher accuracy and improved multi-class F1 scores compared to existing models. The model's color-coded segmentation outputs offer interpretable visual representations, aiding medical professionals in assessing DR severity levels.

Conclusions: The XceSE_SparseLwMLA-UNet model shows promise for advancing early DR diagnosis and management by enhancing classification accuracy and providing valuable visual insights. Its integration of advanced architectural features and transfer learning contributes to better patient care and improved visual health outcomes.

Aims: A bidirectional relationship has been reported between diabetes mellitus and periodontitis. The present study aimed to estimate salivary fructosamine in diabetic and non-diabetic individuals with healthy and diseased periodontium and to measure its changes after non-surgical periodontal therapy. Another aim was to identify the cut-off value of salivary fructosamine to diagnose diabetes mellitus and to correlate it with glycated hemoglobin.

Methods: Salivary fructosamine and HbA1c were assessed in periodontally healthy individuals and periodontitis patients (n = 60 in each group). Both groups comprised of equal number of patients with and without diabetes mellitus. Salivary fructosamine estimation was repeated 4 weeks after non-surgical periodontal therapy in periodontitis patients.

Results: HbA1c and Salivary fructosamine were significantly higher in the periodontally diseased compared to the healthy group. Significantly higher values of these biomarkers were noticed in diabetic patients with periodontitis compared to the non-diabetic group. Periodontal therapy significantly reduced salivary fructosamine in both diabetic and nondiabetic periodontitis patients. A significant positive high correlation was noticed between salivary fructosamine and HbA1c (r = 0.76). The cut-off value of salivary fructosamine was found to be 68 µg/mL with 95% sensitivity, 81.67% specificity, 83.82% positive predictive value, and 94.23% negative predictive value.

Conclusion: Periodontitis can contribute to glycemic control and periodontal therapy can bring about improvement in glycemic status. Salivary fructosamine could be used as an alternate glycemic biomarker and its advantages over HbA1c include simple and non-invasive collection of saliva and it can provide intermediate glycemic status.

Clinical trial registry of india: 2020/11/038496.

Aims: To monitor fetal size and identify predictors for birthweight in women with gestational diabetes (GDM) and normal glucose tolerance (NGT).

Methods: Cohort study of 1843 women universally screened for GDM, with routine ultrasounds each trimester. Women with GDM and NGT were categorized in subgroups by birthweight centile.

Results: Of the total cohort, 231 (12.5%) women were diagnosed with GDM. Fetal size, incidence of large-for-gestational age (LGA: 12.3% of GDM vs. 12.9% of NGT, p = 0.822) and small-for-gestational age (SGA) neonates (4.8% of GDM vs. 5.1% of NGT, p = 0.886) were similar between GDM and NGT. GDM women with LGA neonates were more insulin resistant at baseline and had more often estimated fetal weight (EFW) ≥ P90 on the 28-33 weeks ultrasound (p = 0.033) than those with AGA (appropriate-for-gestational age) neonates. Compared to NGT women with AGA neonates, those with LGA neonates were more often obese and multiparous, had higher fasting glycemia, a worse lipid profile, and higher insulin resistance between 24 -28 weeks, with more often excessive gestational weight gain. On the 28-33 weeks ultrasound, abdominal circumference ≥ P95 had a high positive predictive value for LGA neonates in GDM (100%), whereas, in both GDM and NGT, EFW ≥ P90 and ≤ P10 had a high negative predictive value for LGA and SGA neonates (> 88%), respectively.

Conclusions: There were no differences in fetal size throughout pregnancy nor in LGA incidence between GDM and NGT women. EFW centile at 28-33 weeks correlated well with birthweight. This indicates that GDM treatment is effective and targeted ultrasound follow-up is useful. TRIAL REGISTRATION CLINICALTRIALS.GOV: NCT02036619. Registration date: January 15, 2014. https://clinicaltrials.gov/ct2/show/NCT02036619 .