Diabetology, Volume 5, Issue 1 (March 2024) – 10 articles

(1) Background: It has been previously shown that sourdough bread, compared to commercial yeast bread, elicits a lower postprandial glycemic and insulinemic response in patients with impaired glucose tolerance (IGT). Aims: Our aim was to evaluate the following aspects in persons with type 2 diabetes (T2DM): (1) glucose variations induced by three different doughs: X = bread prepared with functional alkaline biocrystal water, Y = sourdough-leavened bread, and W = bakery yeast bread; (2) the utility of flash glucose monitoring (FGM) to measure GL. (2) Methods: Twelve T2DM following diets (six males, diabetes duration 10.9 ± 1.3 years with no complications, Hba1c < 7.0%), after 12 h of fasting, consumed 180 g of the study breads leavened/matured for 48 (X), 8 (Y), and 4 h (W) at room temperature with 200 mL of water, in a random order, in single-blind conditions, on three different days. All patients had FGM running for the entire period of the experiments. Insulin was determined by capillary blood obtained for the basal and peak glucose concentrations. (3) Results: The peak glucose and peak insulin concentrations were significantly (p < 0.05) higher for W versus both X and Y, without significant differences between X and Y. The area under the curve of glucose variations for over 240 min was significantly higher in W than X (p < 0.01) and Y (p < 0.05), without significant differences between X and Y. (4) Conclusions: (1) Bread prepared with biocrystal water has the same lower GL of sourdough bread compared to bakery yeast bread, and it is easier to manage its leavening/maturation period; (2) FGM is a reliable method for determining rapid glucose changes in response to a carbohydrate meal in persons with type 2 diabetes. Full article

The number of food-insecure individuals with diabetes is on the rise. FOODRx is a supplemental healthy food intervention program that gave disease-appropriate food boxes to food-insecure patients with diabetes at their care clinic and included nutrition and recipe materials in the patient’s preferred language (English, Spanish, or Somali). Implemented over a twelve-month period, we analyze FOODRx participants’ pre and post clinical measures, health care usage, and program/clinic satisfaction, and found that participation was linked to post improvements in fasting glucose and HgbA1c levels, reductions in ER visits and healthcare costs, and a decline in patients choosing between medication and food. Glucose levels decreased from 214 to 187 mg/dL and HgbA1c levels decreased from 9.6% to 9.1%. Average ER visits dropped from 1.21 to 1 visit and the reductions in healthcare costs were reflected in a decrease of an average of USD 250,000 in insurance claims. Patients were less likely to experience food insecurity, as measured in number of meals skipped and levels of hunger. Finally, the program improved patient satisfaction with the cultural responsiveness of the information shared with them. Full article

Common dysglycemia measurements including fasting plasma glucose (FPG), oral glucose tolerance test (OGTT)-derived 2 h plasma glucose, and hemoglobin A1c (HbA1c) have limitations for children. Dynamic OGTT glucose and insulin responses may better reflect underlying physiology. This analysis assessed glucose and insulin curve shapes utilizing classifications—biphasic, monophasic, or monotonically increasing—and functional principal components (FPCs) to predict future dysglycemia. The prospective cohort included 671 participants with no previous diabetes diagnosis (BMI percentile ≥ 85th, 8–18 years old); 193 returned for follow-up (median 14.5 months). Blood was collected every 30 min during the 2 h OGTT. Functional data analysis was performed on curves summarizing glucose and insulin responses. FPCs described variation in curve height (FPC1), time of peak (FPC2), and oscillation (FPC3). At baseline, both glucose and insulin FPC1 were significantly correlated with BMI percentile (Spearman correlation r = 0.22 and 0.48), triglycerides (r = 0.30 and 0.39), and HbA1c (r = 0.25 and 0.17). In longitudinal logistic regression analyses, glucose and insulin FPCs predicted future dysglycemia (AUC = 0.80) better than shape classifications (AUC = 0.69), HbA1c (AUC = 0.72), or FPG (AUC = 0.50). Further research should evaluate the utility of FPCs to predict metabolic diseases. Full article

This study aimed to investigate the use of and willingness to adopt web-based technology for self-management of type 2 diabetes among Arabic-speaking immigrants in Saudi Arabia. Conducted in Taif in 2022, it involved participants with type 2 diabetes mellitus, utilizing a study-specific questionnaire to gather data on demographics, disease specifics, and attitudes towards using this technology for diabetes management. Out of the 109 individuals who responded, 91 completed the survey and reported accessing web-based technology and an average usage of two hours per day. The primary use was for social media (90.1%) and information searching (73.6%). The study found a high willingness to use web-based technology for dietary planning (85.7%), physical activity monitoring (94.5%), and communication with healthcare providers (93.41%). Notably, younger participants, those with higher education, and married individuals showed more inclination towards using such technology, as indicated by significant correlations (p < 0.001, CI = 0.03–0.38; p < 0.039, CI = 1.06–10.26; p = 0.024, CI = 1.23–19.74). Over half of the participants (56%) considered web-based technology beneficial for diabetes management, with many finding it time-saving (61.5%). In conclusion, a significant proportion of participants demonstrated a strong preference for integrating web-based technology into their diabetes self-management routines. This preference was particularly evident in key areas such as diet, physical activity, and glucose monitoring. These findings underscore the potential of web-based technologies in supporting effective diabetes management among Arabic-speaking immigrants, highlighting the need for targeted interventions that leverage these digital tools. Full article

Diabetes mellitus (type I and II) is an advancing global health problem, concerningly increasing in prevalence in most of the developed and developing world. Current therapies, such as the subcutaneous injection of insulin, are invasive and require a close monitoring of blood glucose levels to prevent hypo- or hyperglycaemia from occurring. Despite an inexorable search for a cure since Banting and Best discovered and purified insulin in 1921, insulin remains a solitary insula, still the gold standard for treatment of type I and late-stage type II diabetes mellitus. Apropos of complications, diabetes causes a myriad of secondary maladies, ranging from diabetic kidney disease, diabetic retinopathy and diabetic neuropathy to erectile dysfunction and peripheral vascular disease. While scientists continue to interminably tinker with perfecting mechanical insulin pumps or dampening the immune response to pancreatic beta cells, an important aspect of the aetiology of diabetes should not be neglected, that of the metabolism. At its heart, diabetes can arguably be considered a metabolic disease, and this review suggests a return to focusing on preventing and treating diabetes by focussing on its metabolic causes. This narrative review summarises the potential of a recent class of synthetic peptides, the melanocortins, to help prevent and treat the complications of diabetes mellitus. The review summarises recent work showing the potential benefits of the melanocortins in treating diabetic complications through various pathways. Full article

Background: The number of people with type 2 diabetes is increasing daily, and therefore, effective therapy is needed to successfully regulate glycemia and reduce the risk of associated complications. Recently, an oral formulation of the glucagon-like peptide-1 receptor agonist (GLP-1 RA) semaglutide has become available. Therefore, the aim of our study was to compare the effectiveness of the new oral formulation and the existing injectable formulation of semaglutide in terms of glycemic and body weight control in a real-world setting. Patients and methods: This was a single-center retrospective observational study conducted at the Rijeka Clinical Hospital Centre. A total of 106 patients with inadequately controlled type 2 diabetes (HbA1c ≥ 7%) on different oral or basal insulin supported oral therapy were enrolled in the study, and data from electronic medical records were retrospectively collected and analyzed from May 2021 to November 2022. All subjects were GLP-1 RA-naive and consequently prescribed 0.5 or 1.0 mg of once weekly injectable semaglutide (IS) or 7 mg or 14 mg of once daily oral semaglutide (OS) for at least 6 months. Glycated hemoglobin (HbA1c), body weight, and body mass index (BMI) were assessed prior to semaglutide administration and after a 6-month follow-up period. The primary endpoint was the change from baseline in HbA1c, and secondary endpoints were the change in body weight and the proportion of participants with a reduction in body weight of ≥5% and ≥10%, respectively, 6 months after the initiation of semaglutide treatment. Results: At the 6-month follow-up, no significant difference was observed between the two formulations in terms of HbA1c reduction (IS −1.1% vs. OS −1.4%, p = 0.126) and weight loss (IS −6.50 kg vs. OS −5.90 kg, p = 0.714). Exactly the same proportion of participants in both groups achieved a weight loss of ≥5% (56.7%, n = 30). A weight loss ≥ 10% was observed in 20.7% (n = 11) of participants administered IS and 15.1% (n = 8) of participants administered OS, respectively (p = 0.454). Conclusion: In a real-world setting, oral semaglutide as an add-on therapy to ongoing antihyperglycemic treatment in patients with inadequately controlled type 2 diabetes who had not previously received GLP-1 RA demonstrated a similar effectiveness as injectable semaglutide in terms of glycemic control and weight loss after 6 months of treatment. Full article

The aim of this study is to analyze the risk factors that lead to cognitive impairment, depression, and psychosocial problems in type 2 diabetes and discern what aspects they have in common. Type 2 diabetes is associated with a higher risk of cognitive impairment, including dementia, which in turn increases the risk of hospitalization, falls, and premature mortality. In this study, we conducted a systematic review to achieve this goal, including searches on electronic databases such as PubMed, Medline, Web of Science, EBSCO Discovery, EBSCO host, Scopus, and ScienceDirect, from 2016 onwards. Additionally, we carried out manual searches in leading journals in the field. After evaluating and analyzing the articles, 60 remained, focusing on the following four main themes: disorders due to biological, psychological, social, and pharmacological causes that lead to neuropsychological complications. Based on the results, consistently analogous risk factors contributing to the onset of cognitive impairments, depression, and psychosocial predicaments encompass comorbid ailments, dysglycemia, gender, heightened levels of apprehension and anxiety, educational attainment, socio-economic standing, and pharmaceutical interventions. Furthermore, in the realm of type 2 diabetes, factors such as disease duration, adiposity, specifically overweight and obesity, and advancing age were also identified as significant contributors to cognitive impairments and depression. Concomitantly, the absence of a robust support system and social network emerged as a shared risk factor, predisposing individuals to psychosocial challenges and depressive states. These findings emphasize that the risk factors for cognitive impairments, depression, and psychosocial issues for type 2 diabetes are similar, highlighting the importance of psychosocial support, education, and patient-centered treatment to optimize outcomes and quality of life. Full article

Background: Diabetes is the primary contributor to cardiovascular disease risk, and when combined with obesity, it further underscores the significance of cardiovascular risk assessment. Methods: A retrospective study of 64 patients with type 2 diabetes (T2D) and obesity on once-weekly subcutaneous semaglutide stratified by cardiovascular risk categories determined using the SCORE2/SCORE2-OP, SCORE2-Diabetes, and ASCVD score calculations. We compare the differences between groups (ASCVD: low + borderline + intermediate versus high-risk group; SCORE2/SCORE2-OP: low + moderate versus high + very high-risk group and SCORE2-Diabetes: low + moderate versus high + very high-risk group) in terms of change from baseline in body mass index (BMI) and HbA1c and weight loss outcomes. Results: Patients in the high-risk group, according to ASCVD risk score, had statistically better results in weight loss ≥ 3%, ≥5%, and ≥10% compared to ASCVD low + borderline + intermediate and without difference regarding HbA1c. According to SCORE2/SCORE2-OP, the high + very high-risk group had statistically better HbA1c and weight loss results but only for ≥5% versus the low + moderate risk group. Based on the score SCORE2-Diabetes, the high + very high-risk group had statistically significant better results in lowering HbA1c and weight loss but only for ≥5% versus the low + moderate risk group. Conclusions: To the best of our knowledge, this study represents the initial investigation linking glycemic control and weight reduction outcomes in individuals with T2D and obesity treated with once-weekly semaglutide stratified by cardiovascular risk categories determined using the SCORE2/SCORE2-OP, SCORE2-Diabetes and ASCVD score calculations. Full article

Diabetes mellitus is a metabolic disorder that has a high global health burden and causes high mortality and morbidity in humans. Medicinal herbs and plants offer a promising alternative to conventional therapies for the management of diabetes. Rosemary (Rosmarinus officinalis L.) is a traditional medicinal herb that has been used for the management of several diseases. Therefore, the present study investigates the antidiabetic properties of diets supplemented with R. officinalis leaf powder on streptozotocin-induced diabetic Wistar rats. First, the phytochemicals and 2,2-dephenyl-1-picrylhydrazyl (DPPH) free-radical scavenging activity of aqueous R. officinalis leaf extract were determined. Streptozotocin-induced diabetic male Wistar rats were fed a diet supplemented with R. officinalis leaf powder (ROP) at 3%, 6%, and 12%, respectively, for 6 weeks. Investigations of food intake, body weight, rat relative organ weights, blood glucose, lipid profiles, creatinine, bilirubin, alanine aminotransferase (ALT), and aspartate aminotransferase (AST) were estimated according to standard procedures. The results show that ROP aqueous extract contains significant amounts of phenolics, flavonoids, and tannins, which exhibit in vitro DPPH free-radical scavenging activity. Based on an in vivo study, ROP reduced blood glucose levels in streptozotocin-induced diabetic animals (p < 0.05). Dietary supplementation with ROP in diabetic rats significantly (p < 0.05) lowered ALT, AST, bilirubin, creatinine, total triglyceride (TG), total cholesterol (TC), and low-density lipoprotein (LDL) levels while increasing high-density lipoproteins (HDLs) when compared with the diabetic control group. Our findings demonstrate that a diet supplemented with R. officinalis leaf powder exhibits an antidiabetic potential with improved health outcomes, as demonstrated by the improved lipid and liver profile enzymes in our animal model. Full article

Introduction: Early detection of type 2 diabetes is essential for preventing long-term complications. However, screening the entire population for diabetes is not cost-effective, so identifying individuals at high risk for this disease is crucial. The aim of this study was to compare the performance of five diverse machine learning (ML) models in classifying undiagnosed diabetes using large heterogeneous datasets. Methods: We used machine learning data from several years of the National Health and Nutrition Examination Survey (NHANES) from 2005 to 2018 to identify people with undiagnosed diabetes. The dataset included 45,431 participants, and biochemical confirmation of glucose control (HbA1c) were used to identify undiagnosed diabetes. The predictors were based on simple and clinically obtainable variables, which could be feasible for prescreening for diabetes. We included five ML models for comparison: random forest, AdaBoost, RUSBoost, LogitBoost, and a neural network. Results: The prevalence of undiagnosed diabetes was 4%. For the classification of undiagnosed diabetes, the area under the ROC curve (AUC) values were between 0.776 and 0.806. The positive predictive values (PPVs) were between 0.083 and 0.091, the negative predictive values (NPVs) were between 0.984 and 0.99, and the sensitivities were between 0.742 and 0.871. Conclusion: We have demonstrated that several types of classification models can accurately classify undiagnosed diabetes from simple and clinically obtainable variables. These results suggest that the use of machine learning for prescreening for undiagnosed diabetes could be a useful tool in clinical practice. Full article