Humans with type 2 diabetes and/or metabolic dysfunction-associated steatotic liver disease (MASLD) are at higher risk of ST-segment elevation myocardial infarction (STEMI) and worse prognosis. However, mechanisms, prognostic factors and risk subtypes in humans with STEMI and (pre)diabetes with or without MASLD, are not fully understood. The DIabetes and ST-segment Elevation Myocardial Infarction (DISTEMI) study is a prospective longitudinal cohort study, recruiting humans with different degrees of glucose tolerance after recent STEMI. This cohort study has the primary objective to detect changes in glycemia and insulin sensitivity derived from the oral glucose tolerance test (OGTT) and their relationships to cardiac function. Secondary objectives address tissue-specific insulin sensitivity and organ function, focusing on adipose tissue, liver and heart. Exploratory objectives comprise multiomic analyses and measures of mitochondrial function and quality of life. At 2 and 12months after STEMI, participants undergo comprehensive cardiometabolic phenotyping (OGTT, modified Botnia clamp-test, magnetic resonance imaging/spectroscopy/elastography, high-resolution respirometry). Magnetic resonance-based techniques are employed to assess cardiovascular function and structure, adipose tissue distribution, skeletal muscle and hepatic lipid deposition and fibrosis, and hepatic energy metabolism. Exploratory analyses include multiomics of blood, urine, and stool samples. Multiomics analyses shall allow detecting biomarkers for stratification of cardiovascular disease risk. Currently, 100 participants have been included in DISTEMI, of whom 29% have type 2 diabetes. The DISTEMI study integrates comprehensive cardiometabolic phenomic with multiomic profiling to identify cardiometabolic STEMI subtypes and predictors of outcomes, and to improve precision risk stratification and targeted prevention. NCT05046483.

Objectives: Pre-diabetes (PD) is a global health concern, with up to 50% of cases progressing to type-II diabetes mellitus within 5 years if untreated, leading to complications such as kidney failure, blindness, neuropathy and foot issues. Material and Methods: An exploratory, randomised, comparative study was conducted over 6 months at the outpatient department of Metropolitan Homoeopathic Medical College and Hospital, Kolkata. Pre-diabetic adults with glycated haemoglobin (HbA1c) between 5.7% and 6.4%, impaired fasting glucose (IFG) between 100 and 125 mg/dL, and/or impaired oral glucose tolerance (IGT) between 140 and 199 mg/dL were randomised to receive individualised homoeopathic medicines (IHMs) within Avogadro Limit or potencies within 12CH/24X (IHMs-WA, n = 35), otherwise beyond Avogadro limit or beyond 12CH/24X (IHMs-BA, n = 35) for 3 months. The primary outcome was HbA1c, with IFG and IGT as secondary outcomes, measured at baseline and after 3 months. Results: Of 154 screened participants, 70 were included in the intention-to-treat analysis. After 3 months, intergroup differences were not statistically significant in HbA1c ( p = 0.37), IFG (F1,68 = 1.8, p = 0.18), IGT (F1,68 = 0.56, p = 0.456) and intra-group differences were shown notable significance in both groups in HbA1c (IHMs-WA: p < 0.01; IHMs-BA: p < 0.001), IFG (IHMs-WA: p < 0.05; IHMs-BA: p < 0.001) and IGT (IHMs-WA: p < 0.01; IHMsBA: p < 0.001). After 3 months, a lower progression rate from PD to diabetes in IHMs-BA group (8.6%, 11.4% in IHMs-WA), and higher rate of conversion to non-diabetic status in IHMs-BA (43.0%, 31.4% in IHMs-WA) were observed with inter-group non-statistical significance differences ( p < 0.05). No adverse events were reported. Conclusion: This exploratory, randomised, comparative trial demonstrated no statistically significant inter-group differences; while demonstrating significant intra-group improvements, and higher percentage of participants progressing to non-diabetic status in both groups. This may suggest that IHMs administered beyond the Avogadro limit (comparative higher dilution) are equivalent to those the within limit (comparative lower dilution), in their efficacy on glycaemic control. Although, observed slightly more promising trend in magnitude of improvement on IHMs-BA, require validation through further robust, long-term, multi-centric, blinded superiority trials with larger sample sizes.

Prediabetes (PD) does not always progress to type 2 diabetes; however, lifestyle modification and weight loss are recommended to prevent disease progression. This study aimed to determine whether a text message-based intervention for older adults with PD in South Korea could reduce glycated hemoglobin (HbA1c) levels and promote healthy habits. A randomized controlled trial with two parallel groups was conducted and approved by the Institutional Review Board of Wonju Severance Christian Hospital (IRB No: CR321379). A text message intervention was provided only to the intervention group for six months, while the control group was advised to maintain their usual daily routines. The text messages covered the following categories: nutrition, exercise, medical knowledge, lifestyle, and self-reflective thoughts. A total of 96 participants aged 50 years and older underwent clinical measurements, completed the Korean Health Habits Questionnaire, and were assessed for quality of life at baseline and after the intervention. HbA1c, waist circumference, and fasting blood glucose decreased significantly in both groups (p < 0.05). The intervention group demonstrated significant improvements in body mass index, low-density lipoprotein cholesterol, total cholesterol, and milk consumption. In contrast, instant food consumption increased significantly in the control group, resulting in significant between-group differences (p < 0.05). In addition, the frequency of late-night eating increased significantly within the control group (p < 0.05). Text message-based interventions may serve as an effective adjunctive self-management strategy to improve HbA1c levels and promote healthy habits in older adults with prediabetes.

Cystic fibrosis-related diabetes (CFRD) is a common extrapulmonary manifestation of cystic fibrosis (CF) affecting up to 50% of adults. Cystic fibrosis-related diabetes is associated with poorer lung function, lower nutrition, and increased mortality. Abnormal blood glucoses, especially post-prandial hyperglycemia, can precede a diagnosis of CFRD by many years, and even the prediabetic state has been associated with poorer health outcomes in people with CF (pwCF). With the advent of cystic fibrosis transconductance regulator (CFTR) modulator therapies, the clinical landscape of CF is changing. With increasing longevity, the prevalence of CFRD is anticipated to rise. Continuous glucose monitoring (CGM) technology has been applied in research and clinical settings for insights into CFRD pathophysiology, and its use for early dysglycemia detection in the CF population is increasing. However, guidance around management of these early glucose abnormalities is limited. This article aims to review and summarize the current literature on use of CGM in the prediabetic state in pwCF and to highlight ongoing areas of research need.

Background: Fetal macrosomia is associated with increased maternal and neonatal morbidity. Early detection relies on adequate Antenatal Care (ANC), including appropriate metabolic screening and serial fetal growth assessment. Suboptimal ANC may delay the recognition of maternal risk factors and fetal overgrowth, particularly in high-risk pregnancies. Case presentation: We reported a case of a 46-year-old Indonesian multiparous woman with obesity who was referred from the Community Health Centre (CHC) to a secondary hospital due to post-term pregnancy, suspected fetal macrosomia, and advanced maternal age. Despite multiple ANC visits, random blood glucose testing was not documented during Integrated Antenatal Care at the CHC, the primary healthcare level. Progressive excessive maternal weight gain and marked increases in fundal height were observed during the second and third trimesters without further metabolic evaluation. An elective caesarean section was performed at 40+4 weeks of gestation, delivering a male neonate weighing 5,295 g. Post-operative evaluation revealed maternal prediabetic status, while the neonate required monitoring due to macrosomia but remained clinically stable. Conclusion: This case highlights the consequences of suboptimal antenatal screening and surveillance in high-risk pregnancies. Failure to perform a timely metabolic assessment and respond to clinical indicators of excessive fetal growth may contribute to the delayed diagnosis of fetal macrosomia. Strengthening the quality and completeness of integrated antenatal care, particularly at the primary healthcare level, is essential to improve early detection and prevent adverse maternal dan neonatal outcomes.

Mitochondrial dysfunction contributes to diabetic cardiomyopathy, yet how genetic predisposition and diet interact to reshape cardiac metabolism in diabetic and prediabetic states remains unclear. The Cohen diabetic rat model, comprising diabetes-resistant (CDr) and diabetes-sensitive (CDs) strains, provides a unique platform to dissect this interplay. Here, we present an integrative global proteomic and bioenergetic characterization of cardiac tissue from CDr and CDs rats fed either a regular or a diabetogenic diet. Proteomic pathway mapping revealed downregulation of cytochrome c oxidase (CcO) subunits, strain-dependent rewiring of fatty-acid oxidation pathways, and CcO subunits switch from "heart-type" to "liver-type" isoforms in the sensitive strain. These changes were accompanied by impaired mitochondrial respiration, ATP depletion, and disruption of mitochondrial quality-control mechanisms, together with increased accumulation of tyrosine 304 phosphorylation of cytochrome c oxidase subunit I, indicative of inflammation-driven regulatory inhibition in a diet-specific manner. These findings establish an understanding of how genetic susceptibility and diet contribute to cardiac mitochondrial dysfunction in the Cohen diabetic rat model.

Course of HbA1c in prediabetic conditions in pediatric endocrine practice

Corneal confocal microscopy is a valuable technique for assessing neuropathy; however, whether it can distinguish painful from painless neuropathy remains uncertain and existing evidence is based on the results of smaller studies. This study assessed the association of corneal nerve parameters with abnormalities identified by electromyography (EMG) and neuropathic pain in a large population with and without (pre)diabetes. In this study we included cross-sectional data for 3425 participants from the Maastricht Study. Wide-field corneal confocal microscopy (WF-CCM) was performed using fully automated analysis of three corneal nerve parameters: corneal nerve branch density (CNBD), corneal nerve fibre density (CNFD) and corneal nerve fibre length (CNFL). An axonal degeneration composite score comprising compound muscle action potential amplitudes (peroneal and tibial) and the sensory nerve action potential amplitude of the sural nerve was created by categorising EMG amplitudes as normal or indicating minor (≤10th percentile), moderate (≤5th percentile) or severe (≤2.5th percentile) abnormalities. Neuropathic pain was determined as a modified Douleur Neuropathique en 4 Questions (DN4) interview score ≥3. The mean age of the participants was 59.2 years; 51.6% were female, 15% had prediabetes (defined as impaired fasting glucose, impaired glucose tolerance or both) and 19% had type 2 diabetes. The median diabetes duration was 3.0 years. Regression analyses revealed statistically significant associations between the axonal degeneration EMG score and WF-CCM parameters (CNFL: β=-0.51 [95% CI -0.78, -0.24]; CNFD: β=-1.56 [95% CI -3.04, -0.08]; CNBD: β=-3.08 [95% CI -5.51, -0.64]; all p<0.05) but no statistically significant associations between neuropathic pain and WF-CCM parameters (CNFL: β=-0.06; CNFD: β=-1.15; CNBD: β=-0.22; all p>0.1). The study found associations between the axonal degeneration EMG score and WF-CCM, but no associations were observed between neuropathic pain and WF-CCM parameters, suggesting that WF-CCM has limited value in assessing neuropathic pain.

This study aimed to determine the association between trajectories of obesity status and prediabetes reversion to normoglycemia or progression to diabetes. The study included 14,452 participants from the National Health Insurance Service-National Health Screening (NHIS-HEALS) cohort who continuously had prediabetes glycemic status during the index period (2002-2008), defined by their fasting plasma glucose. The exposure of the study was the trajectories of obesity (defined by body mass index ≥ 25 kg/m2) generated using latent class growth analysis. The outcomes were reversion to normoglycemia or progression to diabetes, whichever occurred first during the follow-up period (2009-2016). The association between trajectories and changes in prediabetes status were examined using cause-specific hazard regression by obtaining the hazard ratio (HR) with a 95% CI. We identified three distinct trajectories which were “Stable obese”, “Stable non-obese” and “Obese to non-obese”. After a median follow-up of 2 years, 51.99% of participants had their glycemic status back to normoglycemia and 32.17% developed diabetes. Compared with participants in the “Stable obese” group, those in “Stable non-obese” and “Obese to non-obese” groups were more likely to have reversion to normoglycemia (HR with a 95% CI = 1.30 [1.23-1.37] and 1.15 [1.07-1.24], respectively) and lower risk of developing diabetes (0.78 [0.73-0.84] and 0.90 [0.82-0.98], respectively). The findings suggest that maintaining or achieving a non-obese status is linked to higher reversion to normoglycemia as well as lower risks of developing diabetes.

Objective: Impaired Fasting Glucose (IFG), a state of prediabetes characterized by elevated fasting glucose levels, signifies a significantly increased risk of developing Type 2 Diabetes Mellitus (T2D) and associated cardiovascular complications. This comprehensive review systematically analyzes evidence from original research, including Randomized Controlled Trials (RCTs), prospective and retrospective cohort studies, and single- and multi-center implementation experiences, to evaluate the effectiveness and translation challenges of interventions targeting IFG progression. Methods: A targeted literature search was conducted across major medical databases, focusing on human intervention studies published between 2000 and 2025. Included articles were categorized based on intervention type (Intensive Lifestyle Intervention [ILI] or pharmacological) and study design, with an emphasis on T2D incidence rate reduction as the primary outcome. Key Findings: The analysis confirms that ILI, focused on diet, physical activity, and achieving moderate weight loss (5–7%), remains the most efficacious long-term strategy, demonstrating superior and sustained T2D risk reduction (e.g., 58% in the Diabetes Prevention Program) (Diabetes Prevention Program Research Group, 2002); (Tuomilehto et al., 2001). Metformin therapy serves as a critical, cost-effective pharmacological alternative, particularly for high-risk subgroups (e.g., younger individuals, high BMI). Translational studies, including retrospective and multi-center experiences, highlight significant barriers to real-world implementation, such as low sustained adherence and scalability challenges within diverse populations. Conclusion: Effective T2D prevention in individuals with IFG necessitates early, risk-stratified intervention. While ILI is the gold standard, future strategies must focus on developing tailored, technologically-supported, and scalable programs to overcome implementation barriers and maximize population-level health impact.

The escalating prevalence of prediabetes in Indonesia, particularly among children and adolescents, necessitates the development of lightweight, adaptable, and cost-effective telemedicine solutions for the noninvasive monitoring of blood glucose levels. Existing systems predominantly employ machine learning and deep learning approaches that require substantial computational resources and stable internet connectivity, limiting their applicability in regions with constrained digital infrastructure. The objective of this study is to develop an artificial intelligence (AI)–driven telemedicine system that employs an expert system to determine prediabetes status by utilizing commercially available smartwatches as noninvasive optical sensors. The methodological approach includes an examination of smartwatch capabilities to identify Bluetooth Low Energy (BLE) sensors, service architectures, and the Generic Attribute Profile (GATT); the development of a Rule-Based Reasoning (RBR) expert system to determine prediabetes status using Fasting Plasma Glucose (FPG) and Postprandial Plasma Glucose (PP2) measurements; and the application of Rapid Application Development (RAD) methods in the development of Flutter-based mobile applications and Laravel Inertia Vue–based web applications. The results of this study demonstrate that the telemedicine system operates in both offline and online modes and incorporates AI functionality on mobile devices and servers without requiring extensive computational resources. All system functionalities successfully passed testing, and the expert system achieved 100% accuracy in determining prediabetes status. In conclusion, the integration of telemedicine and AI-based expert systems provides an effective, economical, and flexible solution that can be widely implemented in Indonesia to reduce the increasing incidence of prediabetes through continuous digital health monitoring.

Background: Nonalcoholic fatty liver disease (NAFLD) is a common metabolic liver illness associated with obesity, insulin resistance, and dyslipidemia. Understanding the clinical and biochemical characteristics of afflicted populations is critical for early detection and treatment. Methods: This cross-sectional study included 70 NAFLD patients with a mean age of 34.2 years, the majority of whom were female (68.6%). Sociodemographic, anthropometric, biochemical, and behavioral data were gathered and examined, including liver enzyme levels, glycemic status, lipid profiles, thyroid function, and fibrosis risk as determined by the NAFLD Fibrosis Score. Results: The findings exhibited a high prevalence of obesity (84.3%) and central adiposity (88.6%). Dysglycemia was present in 65.7% of patients, including diabetes and prediabetic states. Dyslipidemia was common, with elevated triglycerides (74.3%), total cholesterol (62.9%), LDL (42.9%), and low HDL (50%). Liver enzymes were frequently elevated, particularly ALT (90%). Thyroid dysfunction was less common (12.9%). Fibrosis risk was predominantly low, with 90% classified as F0–F2 and no advanced fibrosis detected. Behavioral risk factors such as smoking (11.4%) and betel nut chewing (18.6%) were noted. Conclusions: This young, predominantly female NAFLD population demonstrates significant metabolic abnormalities and liver enzyme elevations but limited fibrosis, indicating an early disease stage. These findings underscore the importance of early metabolic and lifestyle interventions to prevent progression to advanced liver disease.

Background: Advanced glycation end products (AGEs) play a pivotal role in various human pathologies, including aging and metabolic diseases, and their formation may have significant physiological consequences for human health. Fructoselysine (FL) is an intermediate in the formation of AGEs, and its accumulation has been associated with detrimental health effects. Although several chromatographic methods have been developed for AGEs detection and quantification, no mass spectrometry-based approach has previously been established to quantify FL in different human biological matrices. Methods: In this study, we present a novel UHPLC-HRMS/MS method for the identification and quantification of this compound in various biological matrices, including plasma, feces, and urine. Results: The method demonstrates excellent linearity, accuracy, and precision, with limit of detection (LOD) of 0.02 µM and limit of quantification (LOQ) of 0.06 µM. Recovery rates ranged from 95% to 109% and intra- and inter-day relative standard deviations (RSDs) were below 10%, indicating robust analytical performance. The validated method was successfully applied to quantify FL in plasma, feces, and urine samples from healthy individuals. Additionally, given the known association between AGEs and diabetes, we analyzed a small cohort of prediabetic patients and observed elevated circulating levels of FL compared to healthy controls. Conclusions: This study introduces a sensitive and reliable method for the specific detection and quantification of FL in biological samples and provides new insights into early molecular changes associated with prediabetic condition to improve early diagnosis in aging related diseases.

Introduction: Youth obesity is a strong risk factor for prediabetes (PD) and type 2 diabetes. Current criteria for the diagnosis of PD/diabetes, including fasting glucose, 2-h blood glucose after oral glucose tolerance test (OGTT), and HbA1c, have some acknowledged limitations in youth. Continuous glucose monitoring (CGM) offers the opportunity to record daily glucose profiles in a free-living conditions. This study aims to explore how the CGM metrics are related to PD in youths with obesity. Method: Youths with obesity (BMI-for-age > 2SD, age 10-18 years) wore a Freestyle Libre 2 CGM sensor for 2 weeks. Several CGM metrics were measured, including time in tight ranges (TITR) 70-140 and 70-120 mg/dL. All subjects underwent OGTT, and normal glucose tolerance (NGT) and prediabetes (PD) were defined by American Diabetes Association criteria. A nonparametric Wilcoxon rank-sum test was used to compare NGT and PD youths, and logistic regression analysis was performed to investigate the ability of CGM metrics to predict PD. Results: Overall, 84 youths (age 12.6 ± 1.9 years, 42.4% female, BMI 32.8 ± 6.6 kg/m2, HbA1c5.4 ± 0.2%, CGM use >80%) were recruited. HbA1c, blood glucose measured at baseline, 30, 90, and 120 min, and the area under the curve of glucose after glucose load were significantly higher (P value <0.05) in PD than in NGT youths. TITR 70-140 mg/dL and TITR 70-120 mg/dL were significantly (P < 0.05) lower in PD than in NGT youths. No other CGM metrics differed between the two groups. Both TITR 70-140 and 70-120 mg/dL significantly predict PD (P = 0.02), independent of age and sex, though with modest discriminative ability. Conclusions: This exploratory study showed that TITR measured in free-living may aid the identification of PD in youths with obesity, although the discriminative ability of CGM metrics was limited. Future works will focus on the analysis of the concordance of plasma glucose and CGM during OGTT, as well as their predictive performance.

Higher than normal glucose concentrations have been linked to an increased risk of cancer. We aimed to describe the disease trajectories from prediabetes to cancer, accounting for the possible conversion to type 2 diabetes and risk of death. We used the Clinical Practice Research Datalink primary care records linked to hospital and mortality records to identify individuals aged 18-100 years with newly diagnosed prediabetes between Jan 1, 1998, and Nov 30, 2018, in England. Individuals were followed from the diagnosis of prediabetes until death or Nov 30, 2018, with two intermediate outcomes: type 2 diabetes and cancer. In a multistate model, we estimated state occupancy probabilities and lengths of stay (sojourn times) across eight states and seven transitions (eg, one transition from the prediabetes state to the cancer state; or three transitions from the prediabetes state to the type 2 diabetes, cancer, and death state). During a median follow-up of 7·7 years, 163 782 transitions occurred in 328 049 individuals. In both women and men, cancer incidence rates were greater in older individuals (aged ≥75 years at prediabetes diagnosis) and only marginally higher in those who developed type 2 diabetes versus those with prediabetes (over 10 years, the largest differences were 4·1 more cases per 1000 person-years in women and 4·8 more cases per 1000 person-years in men). 10 years after the diagnosis of prediabetes, the probability of remaining in the prediabetes state ranged from 23·2% (men aged ≥75 years at diagnosis) to 72·1% (men aged <55 years), the probability of death following prediabetes ranged from 1·2% (women aged <55 years) to 38·7% (women aged ≥75 years), the probability of developing type 2 diabetes and remaining in this state ranged from 7·9% (men aged ≥75 years) to 24·0% (women aged <55 years), and the probability of developing cancer and remaining in this state ranged from 1·9% (men aged <55 years) to 7·8% (men aged ≥65 to <75 years) in men and women. During the 10 years following the diagnosis of prediabetes, individuals spent between 5·34 years (men aged ≥75 years at diagnosis) to 8·34 years (men aged <55 years) in the prediabetes state. BMI, smoking, socioeconomic status, and ethnicity were associated with occupancy probabilities and sojourn times. The trajectories of type 2 diabetes and cancer following a diagnosis of prediabetes varied substantially by age at prediabetes diagnosis and, to a lesser extent, other sociodemographic and lifestyle factors, with most younger individuals (aged <55 years) remaining in the prediabetes state. Strategies to improve the prevention and early identification of type 2 diabetes and cancer in individuals with prediabetes should be tailored to the age at which prediabetes is diagnosed. National Institute for Health and Care Research (NIHR) Applied Research Collaboration, East Midlands, NIHR Leicester British Retail Consortium, Hope Against Cancer.

Type 2 diabetes (T2D) is one of the most common non-communicable diseases worldwide. Current guidelines for prediabetes or T2D ((pre)diabetes) management include lifestyle modifications with or without medication. Recent evidence has indicated improvement in glycemia with gut-centric interventions (probiotics, prebiotics, or synbiotics) among (pre)diabetic patients. This study aims to explore the effect of a lifestyle intervention (exercise and dietary modification) with/without synbiotics on metabolic, physical, mental and gut health of (pre)diabetic Indian adults compared to standard of care (SOC). The calculated sample-size is 108 participants (including 25% dropouts), both sexes, 25-75years of age, body mass index (BMI)18.5-34.9kg/m2 with either fasting, postprandial glucose or HbA1c above normal as per the American Diabetes Association (ADA, 2024). Participants will be randomized into one of 3 groups: SOC, lifestyle modification with synbiotics (LS+SYN), or lifestyle modification with placebo (LS+PLA). SOC group will follow routine practice, LS+SYN group will receive multimodal lifestyle-intervention (exercise, dietary modifications including a nutritional supplement) with synbiotic supplementation, while LS+PLA group will be enrolled for the same intervention with placebo supplementation for 12weeks. Changes in metabolic, physical, mental and gut health will be compared between the 3 groups. This exploratory randomized trial will assess the effectiveness of a multimodal lifestyle intervention with a gut centric approach in the management of different domains of health among (pre)diabetic adults. Since both lifestyle and gut-centric interventions individually have provided promising results, it is important to understand their combined effect to optimize management of this disease. Clinical trials registry-India (CTRI), registration number: CTRI/2022/08/045096.

Prediabetes Remission and the Subsequent Risk of Pancreatic Cancer: A Nationwide Cohort Study.

Diabetes mellitus is a major comorbidity among patients awaiting coronary artery bypass surgery. A major burden of adult cardiac surgery is spent on coronary revascularization for myocardial ischemia, the most important cause of heart disease in adults worldwide.The objectives were to explore the prevalence of diabetes in patients who are undergoing CABG in our unit. Identify the risk groups preoperatively and proactive optimize glycemic control.The data collection was carried out among patients who are admitted for CABG in our unit at the National Hospital of Sri Lanka, Colombo. Pre-tested an interviewer-administered questionnaire used to collect the data.In our study, we found a 55.4% prevalence of diabetes mellitus, and 12.7% of them were newly diagnosed to have DM, 8.9% of the patients had elevated serum creatinine levels, and 10.4% of the DM patients were already on insulin preoperatively. Almost 32.1% of them had HbA1c more than 7. Most of the patients were followed up by a General Practitioner, 28.6% or a General Physician 26.8%. Only 10.7% of the DM patients were under the care of an Endocrinologist before CABG.The prevalence of DM in CABG patients in our study is 55.4%. Pre-operative HbA1c screening helped us to diagnose pre diabetes and unmasked new patients to have good glycemic control perioperatively and to have good results postoperatively. The endocrinologist’s input was taken as early as possible to optimize the management.

PurposeDiabetes mellitus is a chronic metabolic disorder linked to sedentary lifestyles, which reduces insulin sensitivity and increases hyperglycemia risk. Standard treatments include lifestyle changes and medications. Recently, herbal remedies like Moringa oleifera leaves have gained attention due to bioactive compounds—polyphenols and flavonoids—believed to have antihyperglycemic effects. However, research on their impact on prediabetic conditions remains limited. This study aimed to investigate the effect of Moringa leaf extract on blood glucose levels in sedentary male Wistar rats to explore its potential as a natural antihyperglycemic agent.Patients and MethodsThirty sedentary male Wistar rats, representing a lifestyle-induced prediabetic model, were acclimatized for two weeks and randomized into five groups: Control (K), Metformin (M), and three treatment groups receiving Moringa oleifera leaf extract at doses of 200 mg/kgBW (D200), 400 mg/kgBW (D400), and 800 mg/kgBW (D800). The intervention lasted for 12 weeks. Blood glucose levels—including fasting, postprandial, and time-based measurements—were assessed using an Accutrend Plus device. Data were analyzed with the Shapiro–Wilk, Levene, Kruskal–Wallis, and Mann–Whitney tests (p < 0.05).ResultsMoringa leaf extract demonstrated a dose-dependent reduction in blood glucose levels. In the FBG test, all treatment groups showed significantly lower glucose levels compared to the control (K: 122.8 mg/dL; D200: 85.6 mg/dL; D400: 82.6 mg/dL; D800: 95.4 mg/dL; P=0.015). Similarly, TBG levels were lower in D200 (197.8 mg/dL) and D400 (186.8 mg/dL) compared to K (231 mg/dL; P=0.022). Although the PPBG decreased over time, especially in D200, the differences were not statistically significant (P>0.05).ConclusionAdministration of Moringa leaf powder has been shown to significantly reduce fasting blood glucose levels, but not postprandial or intermittent blood glucose levels.

Diabetes is one of the major health challenges in today’s world, since chronic elevation of blood sugar can cause serious and sometimes irreparable damage to organs such as the heart, kidneys, and nervous system. Early detection of this disease plays a vital role in reducing its complications. However, machine learning and deep learning models often face distrust in medical settings due to their opaque, “black-box” nature. The aim of this study was to combine three machine learning algorithms using stacking and voting methods to propose a model for type 2 diabetes detection, and to increase transparency by using the explainability techniques LIME and SHAP to identify important features. This study used medical data from 768 Pima Indians Diabetes samples, including 8 features such as age, BMI, glucose, insulin, blood pressure, skin thickness, pregnancies, and family history. Data preprocessing included mean imputation for missing or zero values, Min–Max normalization, and classification into “Normal”, “Prediabetes”, and “Diabetes” based on fasting glucose thresholds. Feature selection was performed using Spearman correlation to retain the most relevant variables. A hybrid machine learning model was developed using three base models Neural Network (NN), k-Nearest Neighbors (KNN), and Random Forest (RF) with automated hyperparameter tuning. The outputs of these models were combined via stacking using a logistic regression (LR) meta-model and in parallel using a soft voting method. Nested cross-validation (5 outer and 5 inner folds) was applied to prevent data leakage and ensure robust evaluation. Model interpretability was assessed using LIME for local explanations and SHAP for global feature importance. Decision thresholds and influential feature regions were identified, and model calibration and decision curves evaluated clinical reliability. Models performance was evaluated using accuracy, precision, recall, specificity, F1-score, AUROC, Brier Score (1–B), and Expected Calibration Error (1–E). Statistical reliability was assessed using bootstrap resampling to compute 95% confidence intervals, as well as paired tests to compare the hybrid model with the base models and voting ensemble. Based on the evaluation metrics, the stacking ensemble achieved perfect performance for Class 0, with 100% accuracy, precision, recall, specificity, F1 score, and AUROC, alongside the highest calibration metrics (Brier Score: 99.9, ECE: 98.7). The Random Forest model also excelled, achieving 100% accuracy, precision, recall, specificity, and F1 score for Class 0 and Class 2. In contrast, the KNN model consistently underperformed, particularly for Class 0 (F1: 83.3, Precision: 83.3, Recall: 83.3). The Neural Network demonstrated strong recall for Class 0 (100%), while the voting ensemble showed balanced results but was slightly outperformed by the top ensemble methods. Explainable AI analyses using LIME and SHAP revealed that glucose was the most influential predictor for identifying the Pre-diabetes state. Both methods consistently identified a decision band between 0.35 and 0.47 (corresponding to 100–125 mg/dL) as the transition zone between “Normal” and “Prediabetes”, confirming the model’s alignment with WHO/ADA diagnostic criteria. The stacking model achieved perfect performance and superior calibration, outperforming all other models in type 2 diabetes prediction and classification. Explainability techniques (LIME and SHAP) identified glucose level, body mass index, and blood pressure as key predictive factors. This approach provides an accurate and interpretable tool for clinical decision support in healthcare systems.