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Aims The aim of the present study was to evaluate, by means of a meta-analysis approach, whether new available data, appeared on qualified literature, can support the effectiveness of an association of HbA1c variability with the risk of macro- and/or micro-vascular complications in type 2 diabetes mellitus (T2DM). Methods The meta-analysis was conducted according to PRISMA Statement guidelines and considered published studies on T2DM, presenting HbA1c variability as standard deviation (SD) or its derived coefficient of variation (CV). Literature search was performed on PubMed in the time range 2015–July 2022, with no restrictions of language. Results Twenty-three selected studies fulfilled the aims of the present investigation. Overall, the analysis of the risk as hazard ratios (HR) indicated a significant association between the HbA1c variability, expressed either as SD or CV, and the complications, except for neuropathy. Macro-vascular complications were all significantly associated with HbA1c variability, with HR 1.40 (95%CI 1.31–1.50, p  < 0.0001) for stroke, 1.30 (95%CI 1.25–1.36, p  < 0.0001) for transient ischaemic attack/coronary heart disease/myocardial infarction, and 1.32 (95%CI 1.13–1.56, p  = 0.0007) for peripheral arterial disease. Micro-vascular complications yielded HR 1.29 (95%CI 1.22–1.36, p  < 0.0001) for nephropathy, 1.03 (95%CI 0.99–1.08, p  = 0.14) for neuropathy, and 1.15 (95%CI 1.08–1.24, p  < 0.0001) for retinopathy. For all-cause mortality, HR was 1.33 (95%CI 1.27–1.39, p  < 0.0001), and for cardiovascular mortality 1.25 (95%CI 1.17–1.34, p  < 0.0001). Conclusions Our meta-analysis on HbA1c variability performed on the most recent published data since 2015 indicates positive association between HbA1c variability and macro-/micro-vascular complications, as well as mortality events, in T2DM, suggesting that this long-term glycaemic parameter merits further attention as a predictive, independent risk factor for T2DM population.

When Italy was placed under lockdown to contain the COVID-19 pandemic from 9 March to 18 May 2020, alternative approaches to delivering care-such as telemedicine-were promoted for patients with chronic diseases like diabetes mellitus (DM). The aim of this study was to analyze patients' perception of, and satisfaction with the telehealth services offered during the COVID-19 emergency at an outpatient diabetes care unit in Italy. A cross-sectional survey was conducted on 250 patients with type 2 diabetes mellitus who regularly attended our diabetes care unit. Data were collected by means of telephone interviews, asking patients how they perceived the telehealth services, and their satisfaction with the televisit and computer-based care. A standardized questionnaire was administered: there were questions answered using a five-point Likert scale, and one open-ended question. Patients' demographic, anthropometric and biological data were collected from their medical records. Correlations between patients' characteristics, their perception of telemedicine, and their satisfaction with the televisit were examined. Spearman's rank-order correlation coefficient ρ (rho) and Kendall's rank correlation coefficient τ (tau) were used as nonparametric measures of the strength of the association between the scores obtained for the two ordinal variables, Perception and Satisfaction, and between other clinical parameters. Principal component analysis (PCA) was also used to assess overall links between the variables. Almost half of the interviewees expressed a strongly positive perception of the medical services received, and more than 60% were very satisfied with the telehealth service provided during the COVID-19 emergency. There was a strong correlation between patients' perception and satisfaction ratings (p<0.0001). Duration of disease showed a significant positive correlation with patients' satisfaction with their medical care. By means of PCA, it was found that BMI correlated inversely with both perception and satisfaction. Following a qualitative analysis of patients' answers to the open-ended question, contact with their specialist was important to them: it was reassuring and a source of scientifically correct information about their disease and the association between COVID-19 and diabetes. Based on our telephone interviews, patients appreciated the telehealth approach and were satisfied with it, regardless of the characteristics of their disease. Telemedicine proved essential to avoid interrupting the continuity of care, and therefore had not only clinical, but also psycho-social repercussions.

The worse outcome of COVID-19 in people with diabetes mellitus could be related to the non-enzymatic glycation of human ACE2, leading to a more susceptible interaction with virus Spike protein. We aimed to evaluate, through a computational approach, the interaction between human ACE2 receptor and SARS-CoV-2 Spike protein under different conditions of hyperglycemic environment. A computational analysis was performed, based on the X-ray crystallographic structure of the Spike Receptor-Binding Domain (RBD)-ACE2 system. The possible scenarios of lysine aminoacid residues on surface transformed by glycation were considered: (1) on ACE2 receptor; (2) on Spike protein; (3) on both ACE2 receptor and Spike protein. In comparison to the native condition, the number of polar bonds (comprising both hydrogen bonds and salt bridges) in the poses considered are 10, 6, 6, and 4 for the states ACE2/Spike both native, ACE2 native/Spike glycated, ACE2 glycated/Spike native, ACE2/Spike both glycated, respectively. The analysis highlighted also how the number of non-polar contacts (in this case, van der Waals and aromatic interactions) significantly decreases when the lysine aminoacid residues undergo glycation. Following non-enzymatic glycation, the number of interactions between human ACE2 receptor and SARS-CoV-2 Spike protein is decreased in comparison to the unmodified model. The reduced affinity of the Spike protein for ACE2 receptor in case of non-enzymatic glycation may shift the virus to multiple alternative entry routes.

Background. Diabetic kidney disease affects approximately 40% of patients with type 2 diabetes mellitus (T2DM) and is associated with an increased risk of end-stage kidney disease (ESKD) and cardiovascular (CV) events, as well as increased mortality. Among the indicators of decline in renal function, the eGFR slope is acquiring an increasing clinical interest. The aim of this study was to evaluate, through a systematic review of the literature and meta-analysis of the collected data, the association between the decline of the eGFR slope, chronic complications, and mortality of T2DM patients, in order to understand whether or not the eGFR slope can be defined as a predictive indicator of complications in T2DM. Methods. The review and meta-analysis were conducted according to PRISMA guidelines considering published studies on patients with T2DM. A scientific literature search was carried out on PubMed from January 2003 to April 2023 with subsequent selection of scientific papers according to the inclusion criteria. Results. Fifteen studies were selected for meta-analysis. Risk analysis as hazard ratio (HR) indicated a significant association between all events considered (all-cause mortality, CV events, ESKD, and microvascular events) for patients with steeper eGFR slope decline than subjects with stable eGFR. Calculated HRs (with 95% CI) were as follows: for all-cause mortality, 2.31 (1.70-3.15); for CV events, 1.73 (1.43-2.08); for ESKD, 1.54 (1.45-1.64); and for microvascular events, 2.07 (1.57-2.73). Overall HR was 1.82 (1.72-1.92). Conclusions. An association between rapid eGFR decline and chronic diabetes complications was demonstrated, suggesting that eGFR slope variability significantly impacts the course of T2DM and that eGFR slope should be considered as a predictor for chronic complications in patients with T2DM. According to the obtained results, the therapeutic management of the patient with diabetes should not focus exclusively on glycaemic control, and particular attention should be paid to preserve renal function.

Gestational diabetes mellitus (GDM), a glucose intolerance developing or first recognized during pregnancy, leads to a series of short- and long-term maternal and fetal complications, somehow related to placenta structural and functional changes. The focus and the objective of the present review are to discuss the results which can be obtained by different mass spectrometric approaches in the study of placenta protein profile. Thus, matrix-assisted laser desorption/ionization mass spectrometry (MALDI) has been applied on placenta omogenates before and after one-dimensional electrophoretic separation, followed by tryptic digestion. MALDI imaging was used for direct investigation on the placenta tissue (both maternal and fetal sides). The results showed that some differences among the absolute abundances of some proteins are present for placenta samples from GDM patients. The majority of investigations were carried out by two-dimensional electrophoresis (2DE) followed by LC-MS/MS or, directly by the label-free LC-MSE approach. It should be emphasized that all these techniques were showed differences in the protein expression between the placenta samples from healthy or GDM subjects. 2DE was also employed to separate and compare placental protein levels from GDM and the control groups: differentially expressed proteins between the two groups were identified by MALDI-TOF/TOF mass spectrometry and were further confirmed by Western blotting. The physiopathological significance of the obtained results are reported and discussed in this narrative review. The experimental data obtained until now show that the newest, mass spectrometric approaches can be considered a valid tool to investigate the possible changes of placenta in the presence of GDM.

Background/Objectives: Patients with type 2 diabetes (T2D) are at risk of developing multiple complications, and diabetic polyneuropathy (DPN) is by far the most common. The purpose of the present study was to assess the ability of a new algorithm based on artificial intelligence (AI) to identify patients with T2D who are at risk of DPN in order to move on to further instrumental evaluation with the biothesiometer method. Methods: This is a single-centre, cross-sectional study with 201 consecutive T2D patients recruited at the Diabetes Operating Unit of the ULSS 6 of Padua (Northeast Italy). The individual risk of developing DPN was calculated using the AI-based MetaClinic Prediction Algorithm and compared with the DPN diagnosis provided by the digital biothesiometer method, which measures the vibratory perception threshold (VPT) on both feet. Results: Of the enrolled patients, 107 (53.23%) were classified by AI software as having a low probability of developing DPN, 39 (19.40%) as having a moderate probability, 29 (14.43%) as having a high probability, and 26 (12.94%) as having a very high probability. In 63 of the total patients, biothesiometer measurement showed a VPT ≥ 25 V, indicative of DPN, while 138 patients had a non-pathological VPT value (< 25 V) (prevalence of abnormal VPT 31.34%; prevalence of normal VPT 68.66%). The overall agreement between biothesiometer results and AI risk attribution was 65%. Cohen’s κ was 0.162, and Gwet’s AC1 coefficient 0.405. Conclusions: The use of an optimized AI algorithm can help estimate the risk of developing DPN, thereby guiding more targeted and in-depth screening, including instrumental assessment using the biothesiometer method.

There is a growing debate in the literature on whether glucose variability contributes, as well as high HbA1c levels and longstanding diabetes, to the onset and progression of diabetic retinopathy (DR) in patients with diabetes types 1 (DM1) and 2 (DM2). Few data, obtained only by self-monitoring of blood glucose, support this hypothesis. We used continuous glucose monitoring (CGM) to investigate the association between DR and glucose variability parameters (SD, CONGA 2, MAGE), acute hyperglycemia (HBGI) and chronic exposure to glucose (AG and AUC tot). We studied 68 patients from 19 to 69 years old, 35 with DM1 and 33 with DM2. The prevalence of retinopathy was 43 % in DM 1 patients and 39 % in DM 2 patients. The values of all indicators were obtained by CGM for 72 h. DR was diagnosed on direct or indirect ophthalmoscopic examination, after inducing mydriasis with tropicamide. HbA1c was measured at the baseline and 6 weeks after CGM to test the stability of the patients’ glycemic control. Univariate analysis showed a close association between DR and duration of diabetes (OR 1.11; 1.04–1.19), intensive insulin therapy (OR 5.6, CI 1.14–27.30), SD (OR 1.03; CI 1.01–1.06) and CONGA 2 (OR 1.02; CI 1.00–1.04)—both indicators of variability and HBGI (OR 1.1, CI 1.01–1.18)—a parameter reflecting acute hyperglycemia. There was no significant correlation with HbA1c ( p  = 0.070). Multivariate regression analysis showed that disease duration is the parameter most significantly correlating with DR (OR 1.05; 1.01–1.15). These results reinforce the evidence that longstanding disease is the factor most closely associated with DR. Our data also suggest, however, that glucose variability—regardless of HbA1c—may also have a role as a risk factor for DR, particularly in the case of acute fluctuations (as represented by CONGA 2 and SD) and acute hyperglycemia (as represented by HBGI).

Background This study aimed to evaluate the predictive performance of an artificial intelligence (AI)-based algorithm in estimating the risk of cardio-cerebrovascular complications in patients with type 2 diabetes mellitus (T2D). Methods Medical records of 532 T2D patients from the Diabetology Unit in Padova, Italy, were analyzed using the Metaclinic AI Prediction Module, which estimates the probability of heart and cerebrovascular organ damage. For patients identified as “Very high” ( n  = 63) or “Low” ( n  = 122) risk for heart disease, additional clinical and instrumental data on their cardiac history were collected. The level of agreement between AI predictions and traditional clinical-instrumental diagnostics was assessed using Cohen’s κ coefficient. Results In the “Very high” risk group, the agreement between AI predictions and clinical diagnostics for heart disease was poor (κ = 0.00), while prediction for cerebrovascular disease showed excellent agreement (κ = 0.89). Similarly, in the “Low” risk group, agreement for heart disease remained poor (κ = 0.00), but agreement for cerebrovascular disease was again high (κ = 0.83). Conclusions A marked difference was observed in the algorithm’s performance. While the AI showed strong predictive ability for cerebrovascular complications, it failed to reliably predict heart disease risk. These results suggest that the algorithm may be clinically valuable for cerebrovascular risk assessment but needs refinement for cardiac prediction.

Background Type 2 diabetes mellitus (T2DM) increases the risk of coronary heart disease (CHD) by 2–4 fold, and is associated with endothelial dysfunction, dyslipidaemia, insulin resistance, and chronic hyperglycaemia. The aim of this investigation was to assess, by a multimarker mass spectrometry approach, the predictive role of circulating proteins as biomarkers of cardiovascular damage progression associated with diabetes mellitus. Methods The study considered 34 patients with both T2DM and CHD, 31 patients with T2DM and without CHD, and 30 patients without diabetes with a diagnosis of CHD. Plasma samples of subjects were analysed through a multiplexed targeted liquid chromatography mass spectrometry (LC-MS)-based assay, namely Multiple Reaction Monitoring (MRM), allowing the simultaneous detection of peptides derived from a protein of interest. Gene Ontology (GO) Analysis was employed to identify enriched GO terms in the biological process, molecular function, or cellular component categories. Non-parametric multivariate methods were used to classify samples from patients and evaluate the relevance of the analysed proteins’ panel. Results A total of 81 proteins were successfully quantified in the human plasma samples. Gene Ontology analysis assessed terms related to blood microparticles, extracellular exosomes and collagen-containing extracellular matrix. Preliminary evaluation using analysis of variance (ANOVA) of the differences in the proteomic profile among patient groups identified 13 out of the 81 proteins as significantly different. Multivariate analysis, including cluster analysis and principal component analysis, identified relevant grouping of the 13 proteins. The first main cluster comprises apolipoprotein C-III, apolipoprotein C-II, apolipoprotein A-IV, retinol-binding protein 4, lysozyme C and cystatin-C; the second one includes, albeit with sub-grouping, alpha 2 macroglobulin, afamin, kininogen 1, vitronectin, vitamin K-dependent protein S, complement factor B and mannan-binding lectin serine protease 2. Receiver operating characteristic (ROC) curves obtained with the 13 selected proteins using a nominal logistic regression indicated a significant overall distinction ( p  < 0.001) among the three groups of subjects, with area under the ROC curve (AUC) ranging 0.91–0.97, and sensitivity and specificity ranging from 85 to 100%. Conclusions Targeted mass spectrometry approach indicated 13 multiple circulating proteins as possible biomarkers of cardiovascular damage progression associated with T2DM, with excellent classification results in terms of sensitivity and specificity.

Aims/Introduction Pregnancy complicated by gestational diabetes mellitus (GDM) is characterized by excessive insulin resistance that impairs the metabolism of glucose and lipids. the aim of the study was to examine lipid profiles during pregnancy of women with GDM, and its impact on fetal growth in a multiethnic population. Materials and Methods The study included 322 pregnant women of different ethnicity with GDM attending a clinical unit specializing in metabolic diseases. Results The area under the curve for the 75‐g oral glucose tolerance test and glycated hemoglobin were significantly different among all groups. At the time of being diagnosed with GDM, Asian and African mothers had significantly lower levels of total and low‐density liprotein cholesterol than European mothers (P < 0.001). The trend for high‐density liprotein cholesterol was similar. Triglycerides levels in the Asian group (193.6 ± 65.5 mg/dL) were higher than in the African group (133.2 ± 49.6 mg/dL, P < 0.001), whereas the European group presented intermediate values (175.8 ± 58.8 mg/dL), which differed significantly only versus the African group (P < 0.001). Pre‐partum lipid profiles showed a trend quite similar to that observed at diagnosis. The newborn's birthweight was significantly different, with that of African women (3,437 ± 503 g) being the highest, followed by that of European women (3,294 ± 455 g) and of Asian women (3,006 ± 513 g). The rates of macrosomia showed a trend with higher values in the African group (13.5%), followed by the European group (5.7%, P = 0.1162), whereas that of the Asian group was zero (P = 0.0023 vs African). Conclusions Our data show that lipid profiles in women with GDM differ by ethnicity. The impact of lipid profile on fetal growth is limited and uninfluenced by ethnicity. The study suggests that lipid profiles differ by ethnic origin in gestational diabetes mellitus, but show a common behavior tendency, resembling what happens in non‐diabetic pregnant women, indicating a physiological role of lipoprotein modification occurring during gestation.