Acta Diabetologica最新文献

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 .

Aim: Type 1 diabetes is one of the fastest-growing chronic health conditions. Estimating the incidence rate of childhood type 1 diabetes will allow to aid in adequate planning of health care resources. The study's aim was to assess the incidence rate of type 1 diabetes in children below 15 years of age from Greater Poland (Poland) between 2006 and 2018, and then to compare obtained data to records collected between 1998 and 2003 in pediatric population aged 0-14 years from the same area.

Methods: In this cohort study covering the period from January 1998 to December 2018, data were collected for children and adolescents below 14 years of age with newly diagnosed type 1 diabetes living in Greater Poland. The overall population size was taken from the Statistical Office of Poland. Total, sex-, and age-specific incidence rates per 100,000 person-years were calculated for each calendar year.

Results: Over a 20-year period, the incidence rate of type 1 diabetes in children aged 0-14 years rose around 3.6-fold, from 8.4/100,000 in 1998 to 30.8/100,000 in 2018, with the peak incidence recorded in last year of the study. A clear male predominance of type 1 diabetes was seen in all ages. The rate of type 1 diabetes incidence growth was comparable between all age groups, while the highest incidence rate was mostly observed in children aged 5-9 and 10-14 years.

Conclusions: The incidence of type 1 diabetes in children aged 0-14 years is rapidly increasing in Greater Poland.

Aim: Periodic screening for diabetic retinopathy (DR) is effective for preventing blindness. Artificial intelligence (AI) systems could be useful for increasing the screening of DR in diabetic patients. The aim of this study was to compare the performance of the DAIRET system in detecting DR to that of ophthalmologists in a real-world setting.

Methods: Fundus photography was performed with a nonmydriatic camera in 958 consecutive patients older than 18 years who were affected by diabetes and who were enrolled in the DR screening in the Diabetes and Endocrinology Unit and in the Eye Unit of ULSS8 Berica (Italy) between June 2022 and June 2023. All retinal images were evaluated by DAIRET, which is a machine learning algorithm based on AI. In addition, all the images obtained were analysed by an ophthalmologist who graded the images. The results obtained by DAIRET were compared with those obtained by the ophthalmologist.

Results: We included 958 patients, but only 867 (90.5%) patients had retinal images sufficient for evaluation by a human grader. The sensitivity for detecting cases of moderate DR and above was 1 (100%), and the sensitivity for detecting cases of mild DR was 0.84 ± 0.03. The specificity of detecting the absence of DR was lower (0.59 ± 0.04) because of the high number of false-positives.

Conclusion: DAIRET showed an optimal sensitivity in detecting all cases of referable DR (moderate DR or above) compared with that of a human grader. On the other hand, the specificity of DAIRET was low because of the high number of false-positives, which limits its cost-effectiveness.

Background: Gestational diabetes mellitus is an endocrine and metabolic disorder that appears for the first time during pregnancy and causes varying degrees of short- and/or long-term effects on the mother and child. The etiology of the disease is currently unknown and isobaric tags for relative and absolute quantitation proteomics approach, the present study attempted to identify potential proteins in placental tissues that may be involved in the pathogenesis of GDM and adverse foetal pregnancy outcomes.

Methods: Pregnant women with GDM hospitalised were selected as the experimental group, and pregnant women with normal glucose metabolism as the control group. The iTRAQ protein quantification technology was used to screen the differentially expressed proteins between the GDM group and the normal control group, and the differentially expressed proteins were analysed by GO, KEGG, PPI, etc., and the key proteins were subsequently verified by western blot.

Results: Based on the proteomics of iTRAQ, we experimented with three different samples of placental tissues from GDM and normal pregnant women, and the total number of identified proteins were 5906, 5959, and 6017, respectively, which were similar in the three different samples, indicating that the results were reliable. Through the Wayne diagram, we found that the total number of proteins coexisting in the three groups was 4475, and 91 differential proteins that could meet the quantification criteria were strictly screened, of which 32 proteins were up-regulated and 59 proteins were down-regulated. By GO enrichment analysis, these differential proteins are widely distributed in extracellular membrane-bounded organelle, mainly in extracellular exosome, followed by intracellular vesicle, extracellular organelle. It not only undertakes protein binding, protein complex binding, macromolecular complex binding, but also involves molecular biological functions such as neutrophil degranulation, multicellular organismal process, developmental process, cellular component organization, secretion, regulated exocytosis. Through the analysis of the KEGG signaling pathway, it is found that these differential proteins are mainly involved in HIF-1 signaling pathway, Glycolysis/Gluconeogenesis, Central carbon metabolism in cancer, AMPK signaling pathway, Proteoglycans in cancer, Protein processing in endoplasmic reticulum, Thyroid cancer, Alcoholism, Glucagon signaling pathway.

Discussion: This preliminary study helps us to understand the changes in the placental proteome of GDM patients, and provides new insights into the pathophysiology of GDM.