Search Results (689)

Objectives: Diabetes Mellitus involves demanding challenges that interfere with family functioning and routines. In turn, family and social context impacts individual glycemic control. This study aims to identify this recursive interplay, the mutual influences of family systems and diabetes management. Design: Data was collected through a cross-sectional design comparing patients, aged 22–55, with and without metabolic control. Methods: Participants filled out a set of self-report measures of sociodemographic, clinical and family systems assessment. Patients (91) were also invited to describe their perception about disease management interference regarding family functioning. We first examined the extent to which family variables grouped dataset to determine if there were similarities and dissimilarities that fit with our initial diabetic groups’ classification. Results: Cluster analysis results identify a two-cluster solution validating initial classification of two groups of patients: 49 with metabolic control (MC) and 42 without metabolic control (NoMC). Independent sample tests suggested statistically significant differences between groups in family subscales- family difficulties and family communication (p < 0.05). Binary logistic regression shed light on predictors of explained variance to no metabolic control, in four models: Sociodemographic, Clinical data, SCORE-15/Congruence Scale and Eating Behavior. Furthermore, groups differ on family support, level and sources of family conflict caused by diabetes management issues. Considering only patients who co-habit with a partner for more than one year (N = 44), NoMC patients score lower on marital functioning in all categories (p < 0.05). Discussion: Family-Chronic illness interaction plays a significant role in a patient’s adherence to treatment. This study highlights the Standards of Medical Care for Diabetes, considering caregivers and family members on diabetes care. Full article

Background/Objectives: Diabetic retinopathy is a leading cause of vision impairment worldwide, and the development of reliable automated classification systems is crucial for early diagnosis and clinical decision-making. This study presents a comprehensive comparative evaluation of two state-of-the-art deep learning families for the task of classifying diabetic retinopathy using retinal fundus images. Methods: The models were trained and tested in both binary and multi-class settings. The experimental design involved partitioning the data into training (70%), validation (20%), and testing (10%) sets. Model performance was assessed using standard metrics, including precision, sensitivity, specificity, F1-score, and the area under the receiver operating characteristic curve. Results: In binary classification, the ResNeXt101-64x4d model and RegNetY32GT model demonstrated outstanding performance, each achieving high sensitivity and precision. For multi-class classification, ResNeXt101-32x8d exhibited strong performance in early stages, while RegNetY16GT showed better balance across all stages, particularly in advanced diabetic retinopathy cases. To enhance transparency, SHapley Additive exPlanations were employed to visualize the pixel-level contributions for each model’s predictions. Conclusions: The findings suggest that while ResNeXt models are effective in detecting early signs, RegNet models offer more consistent performance in distinguishing between multiple stages of diabetic retinopathy severity. This dual approach combining quantitative evaluation and model interpretability supports the development of more robust and clinically trustworthy decision support systems for diabetic retinopathy screening. Full article

The retina offers a unique window into both ocular and systemic health, motivating the development of AI-based tools for disease screening and risk assessment. In this study, we present a comprehensive evaluation of six state-of-the-art deep neural networks, including convolutional neural networks and vision transformer architectures, on the Brazilian Multilabel Ophthalmological Dataset (BRSET), comprising 16,266 fundus images annotated for multiple clinical and demographic labels. We explored seven classification tasks: Diabetes, Diabetic Retinopathy (2-class), Diabetic Retinopathy (3-class), Hypertension, Hypertensive Retinopathy, Drusen, and Sex classification. Models were evaluated using precision, recall, F1-score, accuracy, and AUC. Among all models, the Swin-L generally delivered the best performance across scenarios for Diabetes (AUC = 0.88, weighted F1-score = 0.86), Diabetic Retinopathy (2-class) (AUC = 0.98, weighted F1-score = 0.95), Diabetic Retinopathy (3-class) (macro AUC = 0.98, weighted F1-score = 0.95), Hypertension (AUC = 0.85, weighted F1-score = 0.79), Hypertensive Retinopathy (AUC = 0.81, weighted F1-score = 0.97), Drusen detection (AUC = 0.93, weighted F1-score = 0.90), and Sex classification (AUC = 0.87, weighted F1-score = 0.80). These results reflect excellent to outstanding diagnostic performance. We also employed gradient-based saliency maps to enhance explainability and visualize decision-relevant retinal features. Our findings underscore the potential of deep learning, particularly vision transformer models, to deliver accurate, interpretable, and clinically meaningful screening tools for retinal and systemic disease detection. Full article

Nuts are nutrient-dense foods recognized for their complex chemical composition and associated health benefits. This review provides a comprehensive overview of the botanical classification, morphology, production, and consumption patterns of key nut species, including walnuts, almonds, pistachios, pecans, peanuts, cashews, bitter kola, and kola nuts. It emphasizes the fatty acid profiles, noting that palmitic acid (C16:0) is the predominant saturated fatty acid, while oleic acid (C18:1) and linoleic acid (C18:2) are the most abundant monounsaturated and polyunsaturated fatty acids, respectively. The review also details various analytical techniques employed for extracting and characterizing bioactive compounds, which are crucial for assessing nut quality and health benefits. Methods such as Soxhlet extraction, solid-phase microextraction (SPME), supercritical fluid extraction (SFE), gas chromatography (GC-FID and GC-MS), and high-performance liquid chromatography (HPLC) are highlighted. Furthermore, it discusses scientific evidence linking nut consumption to antioxidant and anti-inflammatory properties, improved cardiovascular health, and a reduced risk of type 2 diabetes, establishing nuts as important components in a healthy diet. This review underscores the role of nuts as functional foods and calls for standardized methodologies in future lipidomic and volatilomic studies. Full article

Background: Retinal diseases are becoming an important public health issue, with early diagnosis and timely intervention playing a key role in preventing vision loss. Optical coherence tomography (OCT) remains the leading non-invasive imaging technique for identifying retinal conditions. However, distinguishing between diabetic macular edema (DME) and macular edema resulting from retinal vein occlusion (RVO) can be particularly challenging, especially for clinicians without specialized training in retinal disorders, as both conditions manifest through increased retinal thickness. Due to the limited research exploring the application of deep learning methods, particularly for RVO detection using OCT scans, this study proposes a novel diagnostic approach based on stacked convolutional neural networks. This architecture aims to enhance classification accuracy by integrating multiple neural network layers, enabling more robust feature extraction and improved differentiation between retinal pathologies. Methods: The VGG-16, VGG-19, and ResNet50 models were fine-tuned using the Kermany dataset to classify the OCT images and afterwards were trained using a private OCT dataset. Four stacked models were then developed using these models: a model using the VGG-16 and VGG-19 networks, a model using the VGG-16 and ResNet50 networks, a model using the VGG-19 and ResNet50 models, and finally a model using all three networks. The performance metrics of the model includes accuracy, precision, recall, $F_2$-score, and area under of the receiver operating characteristic curve (AUROC). Results: The stacked neural network using all three models achieved the best results, having an accuracy of 90.7%, precision of 99.2%, a recall of 90.7%, and an $F_2$-score of 92.3%. Conclusions: This study presents a novel method for distinguishing retinal disease by using stacked neural networks. This research aims to provide a reliable tool for ophthalmologists to improve diagnosis accuracy and speed. Full article

Background/Objectives: Recent studies linked a diet rich in ultra-processed foods (UPFs) with depression and diabetes. Although common risk factors, such as aging, are defined for both diseases, how UPFs are associated with the bidirectional relationship between them is not known. This study aimed to investigate whether UPF intake modifies the association between depression and diabetes within the Brazilian adult population. Methods: This cross-sectional analysis utilized data from the 2019 Brazilian National Health Survey, involving over 87,000 adults (aged 18–92 years). Participants provided self-reported data on diabetes and depression diagnoses, dietary habits (assessed by qualitative FFQ), as well as demographic, and socioeconomic variables. Multivariate logistic regression models were used to evaluate the associations, employing two classification methods—UPF1 and UPF2—based on different thresholds of weekly consumption, for high/low UPF intake. Analyses were stratified by age groups to identify variations in associations. Results: There was a significant association between depression and diabetes, especially among participants with high UPF consumption. Models adjusted by demographic characteristics, as well as meat and vegetable consumptions, demonstrated elevated odds ratios (ORs) for diabetes among individuals with depression consuming high levels of UPF, compared to those with a low UPF intake (OR: 1.258; 95% CI: 1.064–1.489 for UPF1 and OR: 1.251; 95% CI: 1.059–1.478 for UPF2). Stratified analysis by age further amplified these findings, with younger individuals showing notably stronger associations (non-old adult group OR: 1.596; 95% CI: 1.127–2.260 for UPF1, and OR: 6.726; 95% CI: 2.625–17.233 for UPF2). Conclusions: These findings suggest that high UPF intake may influence the relationship between depression and diabetes, especially in younger adults. Future longitudinal studies are warranted to establish causality, investigate underlying biological mechanisms, and examine whether improving overall nutrient intake through dietary interventions can reduce the co-occurrence of depression and diabetes. Full article

The present paper investigates the application of convolutional neural networks (CNNs) for the classification of diabetic foot ulcers, using VGG16, VGG19 and MobileNetV2 architectures. The primary objective is to develop and compare deep learning models capable of accurately identifying ulcerated regions in clinical images of diabetic feet, thereby aiding in the prevention and effective treatment of foot ulcers. A comprehensive study was conducted using an annotated dataset of medical images, evaluating the performance of the models in terms of accuracy, precision, recall and F1-score. VGG19 achieved the highest accuracy at 97%, demonstrating superior ability to focus activations on relevant lesion areas in complex images. MobileNetV2, while slightly less accurate, excelled in computational efficiency, making it a suitable choice for mobile devices and environments with hardware constraints. The study also highlights the limitations of each architecture, such as increased risk of overfitting in deeper models and the lower capability of MobileNetV2 to capture fine clinical details. These findings suggest that CNNs hold significant potential in computer-aided clinical diagnosis, particularly in the early and precise detection of diabetic foot ulcers, where timely intervention is crucial to prevent amputations. Full article

Background/Objectives: Diabetic macular edema (DME) is the primary cause of vision loss in people with diabetes, and if untreated, it can result in irreversible macular damage. Both fluorescein angiography (FA), the gold standard, and optical coherence tomography angiography (OCTA) are used for evaluation of this disease. The objective of this study was to compare the diagnostic value of both. Methods: We conducted a comparative analysis of 98 patients aged 18–80 years with significant DME and best-corrected visual acuity ≥0.1 according to the Snellen chart. Participants underwent glycated hemoglobin blood test (HbA1c) and ophthalmological examinations, including OCTA and FA. OCTA 3 × 3 mm scans of superficial (SCP) and deep capillary plexus (DCP) along with FA scans were exported to the Gimp computer program. Size of the foveal avascular zone (FAZ), the number of visible microaneurysms (MAs), and ETDRS report number 11 classification of the images were assessed. Results: FAZ size differed significantly in superficial plexus (0.41 mm²), deep plexus (0.43 mm²) OCTA, and FA (0.38 mm²) (p < 0.001). FAZ size in DCP OCTA closely correlated with that of FA (τ = 0.79, p < 0.001). The total number of MAs visualized in the OCTA was significantly lower than in FA (p < 0.001). ETDRS classification of scans revealed that the level of consistency between the examinations was moderate to very strong. Conclusions: OCTA may be useful in evaluating macular ischemia. It is less sensitive in detecting MAs in DME eyes. FAZ has sharper boundaries and is larger when measured in OCTA. Poor glycemic control results in higher incidence of MAs in macula. Full article

Background: Sodium–glucose cotransporter 2 (SGLT2) inhibitors have transformed type 2 diabetes mellitus (T2DM) management by promoting glucosuria, lowering glycated hemoglobin (HbA1c), blood pressure, and weight; however, their use is limited by genitourinary infections and ketoacidosis. Phytocannabinoids—bioactive compounds from Cannabis sativa—exhibit multi-target pharmacology, including interactions with cannabinoid receptors, Peroxisome Proliferator-Activated Receptors (PPARs), Transient Receptor Potential (TRP) channels, and potentially SGLT2. Objective: To evaluate the potential of phytocannabinoids as novel modulators of renal glucose reabsorption via SGLT2 and to compare their efficacy, safety, and pharmacological profiles with synthetic SGLT2 inhibitors. Methods: We performed a narrative review encompassing the following: (1) the molecular and physiological roles of SGLT2; (2) chemical classification, natural sources, and pharmacokinetics/pharmacodynamics of major phytocannabinoids (Δ⁹-Tetrahydrocannabinol or Δ⁹-THC, Cannabidiol or CBD, Cannabigerol or CBG, Cannabichromene or CBC, Tetrahydrocannabivarin or THCV, and β-caryophyllene); (3) in silico docking and drug-likeness assessments; (4) in vitro assays of receptor binding, TRP channel modulation, and glucose transport; (5) in vivo rodent models evaluating glycemic control, weight change, and organ protection; (6) pilot clinical studies of THCV and case reports of CBD/BCP; (7) comparative analysis with established synthetic inhibitors. Results: In silico studies identify high-affinity binding of several phytocannabinoids within the SGLT2 substrate pocket. In vitro, CBG and THCV modulate SGLT2-related pathways indirectly via TRP channels and CB receptors; direct IC₅₀ values for SGLT2 remain to be determined. In vivo, THCV and CBD demonstrate glucose-lowering, insulin-sensitizing, weight-reducing, anti-inflammatory, and organ-protective effects. Pilot clinical data (n = 62) show that THCV decreases fasting glucose, enhances β-cell function, and lacks psychoactive side effects. Compared to synthetic inhibitors, phytocannabinoids offer pleiotropic benefits but face challenges of low oral bioavailability, polypharmacology, inter-individual variability, and limited large-scale trials. Discussion: While preclinical and early clinical data highlight phytocannabinoids’ potential in SGLT2 modulation and broader metabolic improvement, their translation is impeded by significant challenges. These include low oral bioavailability, inconsistent pharmacokinetic profiles, and the absence of standardized formulations, necessitating advanced delivery system development. Furthermore, the inherent polypharmacology of these compounds, while beneficial, demands comprehensive safety assessments for potential off-target effects and drug interactions. The scarcity of large-scale, well-controlled clinical trials and the need for clear regulatory frameworks remain critical hurdles. Addressing these aspects is paramount to fully realize the therapeutic utility of phytocannabinoids as a comprehensive approach to T2DM management. Conclusion: Phytocannabinoids represent promising multi-target agents for T2DM through potential SGLT2 modulation and complementary metabolic effects. Future work should focus on pharmacokinetic optimization, precise quantification of SGLT2 inhibition, and robust clinical trials to establish efficacy and safety profiles relative to synthetic inhibitors. Full article

Dioscorea species, known as “Yams”, belong to the Dioscoreaceae family. Members of the Dioscoreaceae family are widely distributed across subtropical and tropical regions. They are notable for their high content of starch, dietary fiber, and various bioactive compounds. In addition to serving as a staple food source, these tubers possess significant medicinal value in traditional medicine, particularly for treating diabetes, diarrhea, and various inflammatory diseases. This study aimed to comprehensively summarize the active components and food development potential of Dioscorea species from research over the past decade by searching commonly used databases such as PubMed, Web of Science, Scopus, and Google Scholar. This review highlights the classification of bioactive compounds in Dioscorea spp. using the NPClassifier tool. We discuss 60 representative bioactive metabolites, including terpenoids, phenylpropanoids, carbohydrates, fatty acids, alkaloids, and amino acids. Additionally, we discuss the functional food applications and regulations of Dioscorea spp., which possess antioxidant, anti-inflammatory, anti-diabetic, and anticancer properties. This review is expected to provide scientific ideas for future research related to prioritizing the optimization of extraction technologies, the execution of rigorous clinical trials to confirm therapeutic effects, and the exploration of novel applications of Dioscorea spp. bioactives to fully harness their potential in improving human health. Full article

Background/Objectives: Diabetic foot (DF) is among the leading causes of diabetes-related disability. It is important to maintain regular follow-up and patient education in the prevention and treatment of DF ulcers. In extraordinary situations such as a pandemic, there are disruptions in regular clinical follow-up and patient education, and the effects of this disruption need to be investigated. The aim of this study was to investigate the impact of the pandemic on the clinical condition of patients hospitalised for DF. Methods: Patients were divided into two groups according to the date of admission to the clinic: the pre-pandemic (1 January 2019–11 March 2020) and the pandemic period (12 March 2020–1 June 2021). Comparisons were made between the two groups in terms of DF data and clinical parameters. Data were analysed with SPSS using chi-square, Student’s t-test and Mann–Whitney U analysis. Results: As a result of the screening, data from 125 DF patients (45 pre-pandemic and 80 pandemic) were collected. The DF stage, according to the Wagner classification, was significantly more advanced in patients during the pandemic period (p = 0.015). However, the time between the onset of symptoms and hospitalisation was longer for patients during the pandemic period (p = 0.035). When analysing treatment outcomes, the rate of wound healing was found to be lower (62.2% vs. 30%), and the rate of transtibial amputation was higher (11.2% vs. 20%) during the pandemic period (p = 0.002). Conclusions: This study found that the number of patients hospitalised for DF increased during the pandemic period, as did the severity of the wound, length of admission and radical treatment interventions. Full article

Background/Objectives: Ultra-processed foods (UPFs) constitute a large proportion of the daily energy intake of Europeans, particularly among children and adolescents. High UPFs consumption is associated with poor dietary quality and adverse health outcomes. This study aimed to examine whether high UPFs consumption is associated with metabolic health in children, adolescents, and adults, using data from the I.Family study. Methods: This cross-sectional analysis (2013/2014) included 2285 participants: 147 children (6–9 years), 645 adolescents (10–19 years), and 1493 adults (≥20 years). For the children and adolescents, a metabolic syndrome (MetS) z-score was calculated, consisting of age- and sex-standardized z-scores of WC, HOMA index, HDL-C, TRG, systolic blood pressure (SBP), and diastolic blood pressure (DBP). For the adults, MetS was defined according to the criteria of the International Diabetes Federation Task Force and other societies. The participants completed at least one 24 h recall, from which their UPFs consumption was estimated using the NOVA classification. The consumption levels were divided into age- and sex-specific quintiles based on the relative energy contribution of these foods. Multivariable regression analyses were conducted to evaluate the associations between UPFs consumption and MetS or its components. Results: No statistically significant associations were found between UPFs consumption and MetS or its components in any age group. The effect sizes were negligible across the quintiles (η² = 0.0065 in children, 0.015 in adolescents, and 0.0009 in adults). While the mean MetS score showed little variation, the prevalence of MetS scores above the 90th percentile increased in the highest UPFs quintile among the children. The diet quality decreased with increasing UPFs consumption. Conclusions: UPFs consumption was not associated with MetS or its components across the age groups. However, a decline in diet quality was observed with increasing UPFs intake, highlighting the importance of public health strategies to reduce UPFs consumption and improve dietary patterns, particularly among younger populations. Full article

Background: Type 2 diabetes mellitus (T2DM) is an epidemic chronic disease that affects millions of people worldwide. This study aims to explore the impact of T2DM on the tear proteome, specifically investigating whether alterations occur before the development of diabetic retinopathy. Methods: Flush tear samples were collected from healthy subjects and subjects with preDM and T2DM. Tear proteins were processed and analyzed by mass spectrometry-based shotgun proteomics using a data-independent acquisition parallel acquisition serial fragmentation (diaPASEF) approach. Machine learning algorithms, including random forest, lasso regression, and support vector machine, and statistical tools were used to identify potential biomarkers. Results: Machine learning models identified 17 proteins with high importance in classification. Among these, five proteins (cystatin-S, S100-A11, submaxillary gland androgen-regulated protein 3B, immunoglobulin lambda variable 3–25, and lambda constant 3) exhibited differential abundance across these three groups. No correlations were identified between proteins and clinical assessments of the ocular surface. Notably, the 17 important proteins showed superior prediction accuracy in distinguishing all three groups (healthy, preDM, and T2DM) compared to the five proteins that were statistically significant. Conclusions: Alterations in the tear proteome profile were observed in adults with preDM and T2DM before the clinical diagnosis of ocular abnormality, including retinopathy. Full article

Obesity is a complex, diverse, and multifactorial disease that has become a significant public health concern. It is a modifiable risk factor for developing type 2 diabetes (T2D). The current classification systems rely on anthropometric measurements, such as body mass index (BMI), which cannot capture the physiopathological diversity of this disease. This study aimed to analyze the metabolic signatures of obesity and diabetes using ¹H-nuclear magnetic resonance (NMR). Obese patients with BMI ≥ 25 kg/m² (according to the Asian cut-off value) with different diabetes status scheduled to undergo metabolic-bariatric surgery at three hospitals were prospectively recruited for this study. Plasma samples of 111 obese patients and 26 healthy controls were analyzed by ¹H-NMR. When compared among groups with different diabetes statuses, four clusters with no differences in BMI but different metabolomics profiles were obtained. These clusters highlight intricate metabolic relationships associated with obesity and diabetes. This study demonstrated the benefits of using precision techniques like ¹H-NMR to better early detection, substantially decreasing the risk of developing T2D and its related complications. This study is the first to report on metabolic markers and altered metabolic profiles of T2D and prediabetes among obese Malaysians with a BMI cut-off value for the Asian population. Full article

Background: The PATHFINDER-CHD Registry is a prospective, multicenter, non-interventional registry across tertiary care centers in Germany. The aim is to analyze real-world data on adults with congenital heart defects (ACHD) or hereditary connective tissue disorders who have manifest heart failure (HF), a history of HF, or are at significant risk of developing HF. This analysis investigates the prevalence and clinical impact of overweight and obesity in this unique population. Methods: As of 1st February, 2025, a total of 1490 ACHD had been enrolled. The mean age was 39.4 ± 12.4 years, and 47.9% were female. Patients were categorized according to Perloff’s functional class and the Munich Heart Failure Classification for Congenital Heart Disease (MUC-HF-Class). Results: The most common congenital heart disease (CHD) in this cohort was Tetralogy of Fallot, transposition of the great arteries, and congenital aortic valve disease. Marfan syndrome was the most common hereditary connective tissue disease. Of the patients, 46.1% were classified as overweight (32.8%) or obese (13.3%), while 4.8% were underweight. The highest prevalence of overweight (47.1%) was observed among patients who had undergone palliative surgery, whereas untreated patients showed the highest proportion of normal weight (57.2%). Cyanotic patients were predominantly of normal weight. Patients with univentricular circulation exhibited significantly lower rates of overweight and obesity (35%; p = 0.001). Overweight and obesity were statistically significantly associated with arterial hypertension, diabetes mellitus, and sleep apnea (all p < 0.001). High BMI was linked to increased use of HF-specific medications, including SGLT2 inhibitors (p = 0.040), diuretics (p = 0.014), and angiotensin receptor blockers (p = 0.005). Conclusions: The data highlight the clinical relevance of overweight and obesity in ACHD with HF, emphasizing the need for individualized prevention and treatment strategies. The registry serves as a critical foundation for the optimization of long-term care in this population. Full article