The Republic of Srpska (RS), as a part of the Western Balkans (WB) region, has a higher diabetes prevalence than the EU. This study aims to assess the cost-effectiveness of early treatment of high-risk patients with pre-diabetes and undiagnosed diabetes in our setting. We designed a Markov chain Monte Carlo (MCMC) model which reflects the current International Diabetes Federation (IDF) three-step plan for the prevention of T2DM in those at increased risk. The model captures the evolution of the disease in FINDRISC high-risk patients from normal glucose tolerance (NGT) to impaired fasting glucose (IFG) or impaired glucose tolerance (IGT) and then to T2DM and its complications. We developed two MCMC models, in order to follow the progression of the disease in high-risk cases, ie, when early treatment is undertaken or when it is not undertaken. The health costs and quality adjusted life years (QALY) were discounted at an annual rate of 3%. The key model parameters were varied in one-way and probabilistic sensitivity analysis. Early treatment resulted in increased life expectancy, postponement of the onset of diabetes and increased QALY for all patients. The discounted incremental cost-effectiveness-ratios (ICER) in NGT, IFG, IGT, and T2DM patients were -289.9, 9724.03, -1478.59 and 4084.67 €. In high-risk IGT patients, ICER was the most favorable, being both a cost saving and QALY gaining, with the consistent results confirmed by the sensitivity analysis. The results recommend the acceptance of a new health policy of identifying IGT patients with the use of FINDRISC questionnaire and plasma glucose measurements; providing them with a lifestyle change program; and implementing intensive diabetes treatment, as their disease progresses. Our results are especially significant for the Western Balkan countries, since this was the first cost-effectiveness study of T2DM prevention in this region.

The risk for developing insulin resistance and type II diabetes increases with age. Although lifestyle factors contribute to age-related insulin resistance, aging itself independently reduces insulin sensitivity, partially via an increase in inflammation and cellular senescence. Low-frequency ultrasound (LFU) has been shown to rejuvenate senescent cells and to reduce the proinflammatory senescence-associated secretory phenotype. Because diabetes is more common in aged individuals, there is an increased need to develop effective therapeutics for aged individuals with this condition. This study investigated the effects of LFU treatment on muscle function, blood glucose control, and skeletal muscle gene expression in aged, insulin-resistant, and diabetic mice. Insulin resistance was induced via a high-fat, high-sucrose (HFHS) diet, and diabetes was induced via an HFHS diet plus a low dose of streptozotocin. Insulin-resistant and diabetic mice exhibited impaired glucose metabolism and physical function, as well as an altered transcriptomic profile in skeletal muscle, indicating an increase in inflammation and an immune response. LFU treatment reversed much of the transcriptomic changes that occurred with insulin resistance and diabetes but had no effect on blood glucose control or physical function. LFU demonstrates potential as a noninvasive therapy for reducing inflammation and altering immune cell function in skeletal muscle in insulin-resistant and diabetic populations.NEW & NOTEWORTHY This study introduces low-frequency ultrasound (LFU) as a novel, noninvasive therapy that attenuates insulin resistance- and diabetes-induced transcriptional changes in aged skeletal muscle. LFU primarily reduced inflammatory and immune-related gene expression, potentially by promoting a shift toward an anti-inflammatory (M2) macrophage profile. These findings suggest that LFU may target underlying inflammatory mechanisms of insulin resistance and diabetes in aging muscle.

This study aimed to measure the impact of a community-based lifestyle modification intervention program on the Health-Related Quality of Life (HRQoL) of adults with prediabetes in two Latin American cities.

A quasi-experimental study was conducted with participants aged 30 and above in two Colombian cities between 2018 and 2022. The glycemic status of study participants was determined through the administration of an oral glucose tolerance test. Individuals exhibiting impaired glucose tolerance (IGT) were selected for inclusion in the intervention program. Of the 146 individuals identified with IGT, 91 completed the one-year intervention protocol. HRQoL was assessed utilizing the EQ-5D-3L questionnaire, both before and after the intervention. Logistic regression models were used to calculate the odds ratios (OR) and 95 % confidence intervals (CI), while classification models based on machine learning algorithms were utilized to identify factors associated with favorable changes in health-related quality of life (HRQoL).

In Bogotá D.C., a significant improvement in HRQoL was documented (pre-intervention: 0.69 ± 0.17; post-intervention: 0.76 ± 0.16), attaining the threshold for clinically meaningful change (0.06). No changes in HRQoL were observed in the study participants. Logistic regression analysis revealed that the improvement in HRQoL was statistically significantly associated with sex (OR 8.75; 95 % CI 1.91-40.03), age (OR 11.61; 95 % CI 1.44-93.44), place of residence (OR 29.31; 95 % CI 5.26-163.54), and weight loss (OR 5.56; 95 % CI 1.15-26.76). According to the XGBoost model, return to normal glycemic status emerged as the most important variable for improvements in HRQoL.

Gender, age, place of residence, weight loss, and return to normoglycemic status were identified as significant predictors in lifestyle modification to improve HRQoL among participants at high risk of developing type 2 diabetes.

Numerous studies have reported an association between neutrophils and type 2 diabetes mellitus (T2DM), although this relationship remains unclear.

To investigate the interaction of neutrophils and a dietary intervention on T2DM incidence after 60 months of follow-up.

A comprehensive analysis was conducted on the framework of the CORDIOPREV study, which included 462 patients without T2DM at the beginning of the study, randomly assigned to either a Mediterranean or a low-fat diet; 107 developed T2DM. Absolute neutrophil counts and neutrophil-related ratios were measured.

Kaplan-Meier curves showed that the lowest tertile of basal neutrophils was associated with a reduced likelihood of T2DM incidence when compared to the middle (hazard ratio [HR] = 0.499 [95% CI, 0.287-0.866]) and the highest tertiles (HR = 0.442 [95% CI, 0.255-0.768]) in the overall population, after adjusting for clinical variables. This association only remained significant in patients who followed a Mediterranean diet when comparing the lowest to the middle (HR = 0.423 [95% CI, 0.213-0.842]) and the highest tertiles (HR = 0.371 [95% CI, 0.182-0.762]). The predictive capacity yielded an AUC of 0.711 (95% CI, 0.652-0.769), with neutrophils being the most important variable in the in the model. Decrease in neutrophils over the 60 months was associated with increased insulin sensitivity index (R = -0.31; P = .019), particularly in patients who followed the Mediterranean diet.

These findings suggest that monitoring neutrophils can help prevent the development of T2DM, as a reduction in neutrophil counts could be associated with improved insulin sensitivity. Following a Mediterranean diet might be a potential strategy to reduce the incidence of T2DM by lowering neutrophil levels. Further research is necessary to gain a deeper understanding regarding this mechanism.

This study aimed to examine the association of nutritional literacy levels and sustainable nutritional behaviors with the risk of developing cardiovascular diseases and diabetes in the Turkish adult population.

Sociodemographic information, disease history, nutritional habits, and physical activity levels of 3146 volunteer individuals (male = 1590, female = 1556) between the ages of 40-75 were collected using a questionnaire form and face-to-face interviews. The sustainable nutritional behaviors of the participants were evaluated using Turkish validated scales for Sustainable and Healthy Eating Behavior (SHE) and nutritional literacy levels with the Evaluation Instrument of Nutrition Literacy on Adults (EINLA). Cardiovascular disease risks of the participants were assessed with the Atherosclerotic Cardiovascular Disease (ASCVD) Risk Estimator program and the Heart Score (SCORE) scale and type-2 diabetes risk with the Finnish Diabetes Risk Score (FINDRISC). Each participant's 24-h food consumption record was obtained using the retrospective recall method.

It was determined that ASCVD and SCORE levels were significantly higher in males compared to females. It was observed that individuals with lower cardiovascular and diabetes risk scores had higher educational levels, and the risks increased significantly with age (p < 0.05). Anthropometric measurements such as body mass index, and waist hip circumference were significantly higher in those with higher cardiovascular and diabetes risk scores. Furthermore, in individuals with higher SCORE and FINDRISC levels, SHE and EINLA scores were significantly lower (p < 0.05). It was also observed that SCORE and diabetes risk scores increased with higher energy and macronutrient intakes.

The risk of developing cardiovascular disease and diabetes was associated with sustainable nutritional behaviors and nutritional literacy. It may be suggested that increasing nutritional literacy and encouraging sustainable nutritional behaviors may be effective strategies in the management and reduction of the prevalence of certain chronic diseases.KEY TEACHING POINTSCardiovascular diseases and diabetes are two major chronic conditions that can be managed and treated through proper nutrition.Increased nutritional literacy levels and sustainable dietary habits may result in reduced risk of cardiovascular diseases and diabetes.Nutritionists should assess the patients' nutrition literacy levels and implement sustainable, health-focused nutrition education programs to enhance their understanding of nutrition.

Objective: Metabolic syndrome (MS) is the most important risk factor for Type 2 diabetes mellitus (T2DM) and cardiovascular disease. This study used a retrospective dataset from the First Affiliated Hospital of Shenzhen University and aimed to develop and validate a novel model nomogram based on clinical parameters to predict MS in patients with T2DM. Methods: A total of 2854 patients with T2DM between January 2014 and May 2022 were selected and divided into a training dataset (n = 2114) and a validation dataset (n = 740). This study used multivariate logistic regression analysis to develop a nomogram for predicting MS in patients with T2DM that included candidates selected in the LASSO regression model. The data were set standardized before LASSO regression. The area under the receiver operating characteristic curve (AUC-ROC) was used to assess discrimination in the prediction model. The calibration curve is used to evaluate the calibration of the calibration nomogram, and the clinical decision curve is used to determine the clinical utility of the calibration diagram. The validation dataset is used to evaluate the performance of predictive models. Results: A total of 2854 patients were eligible for this study. There were 1941 (68.01%) patients with MS. The training dataset included 20 potential risk factors of the patient's demographic, clinical, and laboratory indexes in the LASSO regression analysis. Gender, hypertension, BMI, WC, HbA1c, TG, LDL, and HDL were multivariate models. We obtained a model for estimating MS in patients with T2DM. The AUC-ROC of the training dataset in our model is 0.886, and the 95% CI is 0.871-0.901. Similar to the results obtained from the training dataset, the AUC-ROC of the validation dataset in our model is 0.859, and the 95% CI is 0.831-0.887, thus proving the robustness of the model. The prediction model is as follows: logit (MS) = -9.18209 + 0.14406 ∗ BMI (kg/m2) + 0.09218 ∗ WC (cm) + 1.05761 ∗ TG (mmol/L)-3.30013 ∗ HDL (mmol/L). The calibration plots of the predicted probabilities show excellent agreement with the observed MS rates. Decision curve analysis demonstrated that the new nomogram provided significant net benefits in clinical applications. Conclusion: The prediction model of this study covers four clinically easily obtained parameters: BMI, WC, TG, and HDL, and shows a high accuracy rate in the validation dataset. Our predictive model may provide an effective method for large-scale epidemiological studies of T2DM patients with MS and offer a practical tool for the early detection of MS in clinical work.

Type 2 diabetes (T2D) is a complex disease shaped by genetic and environmental factors, including the gut microbiome. Recent research revealed pathophysiological heterogeneity and distinct subgroups in both T2D and prediabetes, prompting exploration of personalized risk factors. Using metabolomics in two Swedish cohorts (n = 1,167), we identified over 500 blood metabolites associated with impaired glucose control, with approximately one-third linked to an altered gut microbiome. Our findings identified metabolic disruptions in microbiome-metabolome dynamics as potential mediators of compromised glucose homeostasis, as illustrated by the potential interactions between Hominifimenecus microfluidus and Blautia wexlerae via hippurate. Short-term lifestyle changes, for example, diet and exercise, modulated microbiome-associated metabolites in a lifestyle-specific manner. This study suggests that the microbiome-metabolome axis is a modifiable target for T2D management, with optimal health benefits achievable through a combination of lifestyle modifications.

Type 2 diabetes (T2D) is becoming one of the leading health problems in Western societies, diminishing quality of life and consuming a significant share of healthcare resources. This study presents machine learning models for T2D diagnosis and prognosis, developed using heterogeneous data from a Spanish population dataset (Di@bet.es study). The models were trained exclusively on individuals classified as controls and undiagnosed diabetics, ensuring that the results are not influenced by treatment effects or behavioral changes due to disease awareness. Two data domains are considered: environmental (patient lifestyle questionnaires and measurements) and clinical (biochemical and anthropometric measurements). The preprocessing pipeline consists of four key steps: geospatial data extraction, feature engineering, missing data imputation, and quasi-constancy filtering. Two working scenarios (Environmental and Healthcare) are defined based on the features used, and applied to two targets (diagnosis and prognosis), resulting in four distinct models. The feature subsets that best predict the target have been identified based on permutation importance and sequential backward selection, reducing the number of features and, consequently, the cost of predictions. In the Environmental scenario, models achieved an AUROC of 0.86 for diagnosis and 0.82 for prognosis. The Healthcare scenario performed better, with an AUROC of 0.96 for diagnosis and 0.88 for prognosis. A partial dependence analysis of the most relevant features is also presented. An online demo page showcasing the Environmental and Healthcare T2D prognosis models is available upon request.

Background and aims Prediabetes is a reversible state of mild hyperglycemia that significantly increases the risk of developing type 2 diabetes mellitus (T2DM). This study aimed to assess knowledge, attitudes, and practices (KAP), risk levels, and associated factors related to prediabetes among adults in the United Arab Emirates (UAE). Methods This cross-sectional study was conducted between February and March 2022 among adults (≥18 years) residing in the UAE, using non-probability convenience sampling. Data were collected through a self-administered online questionnaire using the Knowledge Attitude Practice - Prediabetes Assessment Questionnaire (KAP-PAQ) and the Saudi Diabetes Risk Score (SADRISC). Data analysis was performed using SPSS (IBM SPSS Statistics for Windows, IBM Corp., Version 28, Armonk, NY). Mann-Whitney U and Kruskal-Wallis tests were used for group comparisons, and Spearman correlation assessed associations. Participants with diabetes were excluded from the SADRISC-based risk analysis. Results A total of 414 participants completed the survey, 278 (67.1%) of whom were non-Emirati Arabs. Poor knowledge of prediabetes was observed in 269 (65%) of participants, and 348 (84.1%) reported poor to very poor practices. In contrast, 238 (57.5%) expressed positive to strongly positive attitudes. The SADRISC tool classified 105 (27.8%) non-diabetic participants as having a high risk for prediabetes or undiagnosed T2DM. Nearly half (203, 49.0%) were unaware that prediabetes progression to T2DM is preventable with intervention, and 244 (58.9%) had never checked their blood sugar levels. Higher knowledge levels were associated with better attitudes and practices. Conclusion This study reveals insufficient knowledge and poor health practices regarding prediabetes among UAE adults despite generally positive attitudes. A considerable proportion of participants were at high risk for prediabetes or undiagnosed T2DM. Nearly half were unaware that prediabetes progression is preventable. Despite national efforts, most had never checked their blood glucose levels. Targeted interventions, including more extensive awareness campaigns and screening programs, are crucial to improving knowledge, encouraging preventive behaviors, and enhancing early detection of prediabetes.

We aimed to develop risk models for predicting the onset of developing diabetes and prediabetes in the first-degree relatives (FDRs) of patients with type 2 diabetes, who have normal glucose tolerance (NGT).

In this study, 1765 FDRs of patients with type 2 diabetes mellitus, who had NGT, were subjected to the statistical analysis. Diabetes risk factors, including anthropometric indices, physical activity, fast plasma glucose, plasma glucose concentrations 2-h after oral glucose administration, glycosylated hemoglobin (HbA1c), blood pressure, and lipid profile at the baseline were considered as independent variables. Kaplan-Meier, log-rank test, univariate, and multivariable proportional hazard Cox regression were used for the data analysis. The optimal cutoff value for risk score was created according to the receiver operating characteristic curve analysis.

The best diabetes predictability was achieved by a model in which waist-to-hip ratio, HbA1c, oral glucose tolerance test-area under the curve (OGTT-AUC), and the lipid profile were included. The best prediabetes risk model included HbA1c, OGTT-AUC, systolic blood pressure, and the lipid profile. The predictive ability of multivariable risk models was compared with fasting plasma glucose (FPG), HbA1c, and OGTT. The predictive ability of developed models was higher than FPG and HbA1c; however, it was comparable with OGTT-AUC alone. In addition, our study showed that the developed models predicted diabetes and OGTT-AUC better than the Finnish Diabetes Risk Score (FINDRISC).

We recommend regular monitoring of risk factors for the FDRs of patients with type 2 diabetes as an efficient approach for predicting and prevention of the occurrence of diabetes and prediabetes in future. Our developed diabetes risk score models showed precise prediction ability compared to the FINDRISC in Iranian population.

The incidence of Type 2 Diabetes Mellitus (T2DM) continues to rise steadily, significantly impacting human health. Early prediction of pre-diabetic risks has emerged as a crucial public health concern in recent years. Machine learning methods have proven effective in enhancing prediction accuracy. However, existing approaches may lack interpretability regarding underlying mechanisms. Therefore, we aim to employ an interpretable machine learning approach utilizing nationwide cross-sectional data to predict pre-diabetic risk and quantify the impact of potential risks.

The LASSO regression algorithm was used to conduct feature selection from 30 factors, ultimately identifying nine non-zero coefficient features associated with pre-diabetes, including age, TG, TC, BMI, Apolipoprotein B, TP, leukocyte count, HDL-C, and hypertension. Various machine learning algorithms, including Extreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), Naive Bayes (NB), Artificial Neural Networks (ANNs), Decision Trees (DT), and Logistic Regression (LR), were employed to compare predictive performance. Employing an interpretable machine learning approach, we aimed to enhance the accuracy of pre-diabetes risk prediction and quantify the impact and significance of potential risks on pre-diabetes.

From the China Health and Nutrition Survey (CHNS) data, a cohort of 8,277 individuals was selected, exhibiting a disease prevalence of 7.13%. The XGBoost model demonstrated superior performance with an AUC value of 0.939, surpassing RF, SVM, DT, ANNs, Naive Bayes, and LR models. Additionally, Shapley Additive Explanation (SHAP) analysis indicated that age, BMI, TC, ApoB, TG, hypertension, TP, HDL-C, and WBC may serve as risk factors for pre-diabetes.

The constructed model comprises nine easily accessible predictive factors, which prove highly effective in forecasting the risk of pre-diabetes. Concurrently, we have quantified the specific impact of each predictive factor on the risk and ranked them based on their influence. This result may serve as a convenient tool for early identification of individuals at high risk of pre-diabetes, providing effective guidance for preventing the progression of pre-diabetes to T2DM.

Aims: We developed a prediction model for elevated hemoglobin A1c (HbA1c) among patients presenting to the emergency department (ED) at risk for diabetes to identify important factors that may influence follow-up patient care. Methods: Retrospective electronic health records data among patients screened for diabetes at the ED in May 2021 was used. The primary outcome was elevated HbA1c (≥ 5.7%). The data was divided into a derivation set (80%) and a test set (20%) stratified by elevated HbA1c. In the derivation set, we estimated the optimal significance level for backward elimination using a 10-fold cross-validation method. A final model was derived using the entire derivation set and validated on the test set. Performance statistics included C-statistic, sensitivity, specificity, predictive values, Hosmer-Lemeshow test, and Brier score. Results: There were 590 ED patients screened for diabetes in May 2021. The final model included nine variables: age, race/ethnicity, insurance, chief complaints of back pain and fever/chills, and a past medical history of obesity, hyperlipidemia, chronic obstructive pulmonary disease, and substance misuse. Adequate model discrimination (C-statistic = 0.75; sensitivity, specificity, and predictive values > 0.70), no evidence of model ill fit (Hosmer-Lemeshow test = 0.29), and moderate Brier score (0.21) suggest acceptable model performance. Conclusion: In addition to age, obesity, and hyperlipidemia, a history of substance misuse was identified as an important predictor of elevated HbA1c levels among patients screened for diabetes in the ED. Our findings suggest that substance misuse may be an important factor to consider when facilitating follow-up care for patients identified with prediabetes or diabetes in the ED and warrants further investigation. Future research efforts should also include external validation in larger samples of ED patients.

Undiagnosed diabetes mellitus (UDM) is associated with poorer health outcomes compared to diagnosed DM. We investigated factors associated with UDM in a highly urbanized Chinese setting to facilitate UDM detection.

We analysed data from the cross-sectional Hong Kong Population Health Survey. We defined UDM by blood glucose and HbA1c levels and a negative history of self-reported doctor-diagnosed DM. We categorized diabetes status into UDM, incident DM (IDM, i.e. recently diagnosed) and individuals without diabetes and used multinomial logistic regression models to investigate the relationship between diabetes status and social and health service-related factors.

We included 98 IDM cases, 101 UDM cases, and 2,153 individuals without diabetes. Individuals aged 35-44 years (aOR 12.65, 95% C.I. 2.54-62.97) and those living in subsidized-sale housing (aOR 2.01, 95% C.I. 1.14-3.56) had a higher risk of UDM relative to not having diabetes, but not IDM. Males who were economically active (aOR 4.22, 95% C.I. 1.25-14.30), and males who did not have regular check-ups (aOR 3.05, 95% C.I. 1.16-8.00) had higher risks of UDM relative to not having diabetes, whereas males with a higher household income had a lower risk of UDM (aOR 0.94, 95% C.I. 0.89-0.99). Compared to individuals without diabetes, UDM cases had comparable physical activity levels but most were work- and transport-related rather than recreational.

Compared to individuals without diabetes or IDM cases, economically active males, males without regular check-ups and males with lower household income had a higher risk of UDM. Targeted active DM screening can reduce UDM. However, further research on the benefits of different types of physical activity is needed.

Patients with type 2 diabetes (T2D) often experience prolonged periods of asymptomatic hyperglycemia, which significantly increases the risk of developing chronic complications related to diabetes. Screening programs for individuals at high risk for T2D provide valuable opportunities not only for early diagnosis but also for detecting intermediate hyperglycemic states, commonly referred to as prediabetes. Interventions aimed at preventing diabetes in this group can successfully delay or even avoid the onset of the disease and its associated burdens. This review is an update of the Brazilian Diabetes Society (Sociedade Brasileira de Diabetes [SBD]) evidence-based guideline for diagnosing diabetes and screening T2D.

The methodology was previously published and defined by the internal institutional steering committee. The working group drafted the manuscript by selecting vital clinical questions for a narrative review, utilizing MEDLINE via PubMed to identify relevant studies. The review assessed the best available evidence, including randomized clinical trials (RCTs), meta-analyses, and high-quality observational studies related to the diagnosis of diabetes.

Fifteen specific recommendations were formulated. Screening is recommended for adults aged 35 and older or younger individuals with obesity and additional risk factors. For children and adolescents, screening is recommended starting at age ten or the onset of puberty if they are overweight or obese and have additional risk factors. Fasting plasma glucose (FPG) and HbA1c are recommended as initial screening tests. The oral glucose tolerance test (OGTT) is recommended for high-risk individuals with normal HbA1c and FPG or those with prediabetes. The 1-h OGTT is preferred over the 2-h OGTT, as it is both more practical and a superior test. A structured approach to reevaluation intervals is provided.

Considering the increasing prevalence of diabetes, we aimed to evaluate the risk of diabetes in our sample and its relationship with sociodemographic and nutrition-related factors.

We conducted the study in Afyonkarahisar province of Turkiye with participants aged 18-65 years. In this face-to-face study, we used a questionnaire on sociodemographic information and general dietary habits and the FINDRISC screening tool. We also recorded participants' 24-hour food recall and assessed anthropometric measurements. We analyzed epidemiological data using binary logistic regression models to assess possible risk factors associated with the presence of diabetes risk.

Overall, this study included 3,990 participants, 50.03% (n = 1996) and 49.97% (n = 1994) of whom were males and females, respectively. The FINDRISC score was higher in females (p = 0.001), married individuals (p < 0.001), those with lower education levels (p < 0.001), and participants diagnosed with the disease by a doctor (p < 0.001). Additionally, having a body mass index (BMI) of > 30 kg/m2 increased the risk by 7.33 folds compared with having a BMI of < 25 kg/m2. Significant but very low correlation coefficients were found between main meal consumption, energy, lipid and iron intake and diabetes risk (p < 0.001).

Our findings suggest that increasing age, increasing BMI, lower education level, and having a disease diagnosis can be significant risk factors for diabetes. However, more studies are needed to clarify risk factors, especially those related to nutrition.

Primary prevention of diabetes still remains as an unmet challenge in a real world setting. While, translational programmes have been successful in the developed nations, the prevailing social and economic inequities in the low and middle income countries, fail to integrate diabetes prevention into their public health systems. The resulting exponential increase in the prevalence of diabetes and the cost of treatment has put primary prevention in the back seat. As a call to action, an expert group was formed to lay down practical guidelines for clinicians in the South East Asian regions to implement primary prevention programmes at an individual or at a community level. The guideline was developed based on the outcomes of the evidence based prevention programmes conducted in India. This decentralised self-guided approach for primary prevention of diabetes follows a three step implementation process of screening, diagnosis of intermediate hyperglycaemia and design and delivery of personalized interventions. Recommendations provided on dietary intake and physical activity can be tailored by the clinician to suit individual needs. Initiation of pharmacological treatment to achieve desired targets has also been addressed. A personalised approach by the clinician may be effective and offer a sustainable solution to curb the rising epidemic.

To assess the glycaemic status of Asian patients in a tertiary care dental setting and develop a risk model for undiagnosed diabetes mellitus (DM).

A total of 1074 participants completed a diabetes risk test questionnaire, full-mouth periodontal examination and a point-of-care HbA1c finger-prick blood test. Univariable logistic regression was performed to assess the effect of potential factors to predict DM, with confirmed diabetes as the outcome. Subsequently, multivariable logistic regression analysis with stepwise variable selection was employed to develop the final models for predicting DM.

Sixty-five (6.1%) and 83 (7.7%) of the 1074 participants were medically confirmed with T2DM and prediabetes, respectively. The 'best' predictive risk model for DM included body mass index (BMI), family history of diabetes, smoking and a diagnosis of Stage III/IV or severe periodontitis with an area under the curve (AUC) of 0.717 (95% confidence interval, CI [0.689-0.744]) and 0.721 (95% CI [0.693-0.748]), respectively. Including the oral health measure marginally increased the AUC.

Dental patients clinically diagnosed with advanced periodontitis in combination with high BMI, positive family history of DM and smoking are potentially at high risk for DM and should be screened for DM and referred for medical confirmation and management.

To examine the relationship between inflammatory biomarkers and the occurrence of cardiovascular events in patients with type 2 diabetes mellitus (DM2) and stable coronary artery disease.

A total of 964 patients with stable coronary artery disease were included. Plasma levels of inflammatory markers, including tumour necrosis factor receptors 1 and 2 (TNF-R1 and TNF-R2), growth differentiation factor-15 (GDF-15), soluble suppression of tumorigenicity 2 (sST2), and high-sensitivity C-reactive protein (hsCRP) were measured. The primary endpoint was the development of acute ischaemic events (any type of acute coronary syndrome, stroke, or transient ischaemic attack).

There were 232 diabetic patients and 732 non-diabetic patients. Patients with coronary artery disease and DM2 (232, 24%) had higher levels of TNF-R1, TNF-R2, GDF-15, sST2 (P<.001), and hsCRP compared to patients without DM2, indicating a higher inflammatory state. After a median follow-up of 5.39 (2.81-6.92) years, patients with DM2 more frequently developed the primary endpoint (15.9% vs 10.8%; P=.035). Plasma levels of TNF-R1 were independent predictors of the primary endpoint in patients with DM2, along with male gender, triglyceride levels, and the absence of treatment with angiotensin-converting enzyme inhibitors. None of these inflammatory markers predicted the development of this event in non-diabetic patients.

Patients with stable coronary artery disease and DM2 exhibit elevated levels of the proinflammatory markers TNF-R1, TNF-R2, GDF-15, and sST2. Moreover, TNF-R1 is an independent predictor of acute ischaemic events only in diabetic patients.

To evaluate the predictive value of a contemporary type 2 diabetes (T2D) polygenic score (PGS) in detecting incident diabetes across a range of diabetes risk factors.

We analysed participants in the Further Cardiovascular Outcomes Research With PCSK9 Inhibition in Subjects With Elevated Risk (FOURIER) trial (ClinicalTrials.gov, number NCT0176463), which compared the efficacy of proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitor evolocumab versus placebo in lowering cardiovascular outcomes in participants with stable atherosclerotic cardiovascular disease and LDL cholesterol levels of 70 mg/dL (1.8 mmol/L) or higher who were receiving statin therapy. Genetic risk was characterized using a previously validated T2D PGS based on ~1.2 million single-nucleotide polymorphisms. PGS was analysed continuously and categorically as high (top 20% of the PGS) and low to intermediate (lower 80% of the PGS). The effect of evolocumab on incident diabetes in patients without diabetes at baseline was also assessed. HbA1c was measured at baseline and every 24 weeks thereafter, while FPG was measured at baseline, week 12, week 24 and every 24 weeks thereafter. Potential cases of incident diabetes were adjudicated centrally. Hazards ratios (HRs) for incident diabetes were adjusted for baseline characteristics and ancestry.

Among 9388 participants, the mean age was 63 ± 9 years and 22.7% were women, with median HbA1c 39 mmol/mol (36 mmol/mol - 41 mmol/mol; 5.7% [5.4%-5.9%]) and mean body mass index (BMI) 28.7 ± 5 kg/m2. Diabetes developed in 690 participants (7.3%) during 2.3 years of median follow-up. T2D PGS predicted incident T2D (HR per 1-SD 1.22, 95% CI 1.14-1.32, p < 0.001). The rates of incident T2D in the high and low to intermediate genetic risk categories were 12.1% versus 6.8%, respectively (HR 1.43 95% CI 1.20-1.70, p < 0.001). Notably, high T2D genetic risk had greater predictive strength among individuals with lower HbA1c (P-int = 0.0499) and lower BMI (P-int = 0.004).

The T2D polygenic score serves as an independent predictor of incident diabetes, particularly among individuals with lower distribution of traditional risk factors.

A number of seismic shifts are expected to reshape the future of medicine. The global population is rapidly aging, significantly impacting the global disease burden. Medicine is undergoing a paradigm shift, defining and diagnosing diseases at earlier stages and shifting the health care focus from treating diseases to preventing them. The application and purview of digital medicine are expected to broaden significantly. Furthermore, the COVID-19 pandemic has further accelerated the shift toward predictive, preventive, personalized, and participatory (P4) medicine, and has identified health care accessibility, affordability, and patient empowerment as core values in the future digital health era. This "left shift" toward preventive care is anticipated to redefine health care, emphasizing health promotion over disease treatment. In the future, the traditional triad of preventive medicine-primary, secondary, and tertiary prevention-will be realized with technologies such as genomics, artificial intelligence, bioengineering and wearable devices, and telemedicine. Breast cancer and diabetes serve as case studies to demonstrate how these technologies such as personalized risk assessment, artificial intelligence-assisted and app-based technologies, have been developed and commercialized to provide personalized preventive care, identifying those at a higher risk and providing instructions and interventions for healthier lifestyles and improved quality of life. Overall, preventive medicine and the use of advanced technology will hold great potential for improving health care outcomes in the future.

Diabetes is a global health concern, and early identification of high-risk individuals is crucial for preventive interventions. Finnish Diabetes Risk Score (FINDRISC) is a widely accepted non-invasive tool that estimates the 10-year diabetes risk. This study aims to validate the FINDRISC in the Turkish population and develop a specific model using data from a nationwide cohort.

The study used data of 12249 participants from the Türkiye Chronic Diseases and Risk Factors Survey. Data included sociodemographic variables, lifestyle factors, and anthropometric measurements. Multivariable logistic regression was employed using FINDRISC variables to predict incident type 2 diabetes mellitus (T2DM). Two country-specific models, one incorporating the waist-to-hip ratio (WHR model) and the other waist circumference (WC model), were developed. The least absolute shrinkage and selection operator (LASSO) algorithm was used for variable selection in the final models, and model discrimination indexes were compared.

The optimal FINDRISC cut-off was 8.5, with an area under the curve (AUC) of 0.76, demonstrating good predictive performance in identifying T2DM cases in the Turkish population. Both WHR and WC models showed similar predictive accuracy (AUC: 0.77). Marital status and education were associated with increased diabetes risk in both country-specific models.

The study found that the FINDRISC tool is effective in predicting the risk of type 2 diabetes in the Turkish population. Models using WHR and WC showed similar predictive performance to FINDRISC. Sociodemographic factors may play a role in diabetes risk. These findings highlight the need to consider population-specific characteristics when evaluating diabetes risk.

Early prediabetes screening holds immense significance in decreasing the incidence of diabetes. Therefore, we aimed to evaluate the association of hepatic enzymes with prediabetes and diabetes in the Azar cohort population in Iran.

This cross-sectional study utilized data from the Azar cohort study, initiated in 2014, with 14,865 participants aged 35-70 years. This study defines prediabetes, according to the American Diabetes Association (ADA), as fasting blood sugar (FBS) of 100-125 mg/dl. An FBS ≥ 126 mg/dL or a history of diabetes indicates diabetes. Serum liver enzymes including alanine aminotransferase (ALT), aspartate aminotransferase (AST), gamma-glutamyl transferase (GGT), and alkaline phosphatase (ALP) were measured, and associations with prediabetes and diabetes were analyzed using binary logistic regression.

In a study of 14,865 participants, 16% had prediabetes and 14.1% had diabetes. The serum levels of ALT, AST, GGT, and ALP were significantly higher (P < 0.05) in the prediabetic and diabetic patients. The adjusted logistic regression model showed a dose-response increase for all hepatic enzymes, with the highest ORs in the fourth quartile for both prediabetes and diabetes. The highest OR for prediabetes and diabetes was in the fourth GGT quartile.

Our findings suggest that serum ALT, GGT, and ALP levels are strongly associated with prediabetes and diabetes. These hepatic enzymes may be considered easy and valuable early indicators of diabetes risk, prompting timely interventions to slow disease progression.

This study evaluated the diagnostic accuracy of the Modified Finnish Diabetes Risk Score (Modified FINDRISC) for detecting individuals with metabolic syndrome in Indonesia.

A dataset from the 2018 Indonesian National Basic Health Survey was analysed, and cases of metabolic syndrome were identified in accordance with both National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP III) and International Diabetes Federation (IDF) guidelines. Diagnostic accuracy of the Modified FINDRISC tool was evaluated using the Area Under the Receiver Operating Characteristic (AUC) curve, while optimal cut-off scores were determined by Youden's Index.

From 25,432 participants, the mean and standard deviation of the Modified FINDRISC score was 5.7 (SD 4.1). The prevalence of metabolic syndrome was 32.1% and 24.8% based on NCEP-ATP III and IDF criteria, respectively. Based on NCEP-ATP III criteria alone, the AUC of the Modified FINDRISC was 80.9% (80.3%-81.5%) with 74.0% sensitivity and 75.5% specificity. Similarly, based on IDF criteria, AUC was 88.9% (88.5%-89.3%) with 89.8% sensitivity and 75.8% specificity. The optimal cut-off score was 6 for both criteria, with 41.2% of the total participants above the cut-off who would require further confirmation tests.

Metabolic syndrome is prevalent in Indonesia, and the Modified FINDRISC tool offers good diagnostic accuracy for detecting such cases. Utilising Modified FINDRISC as a first-instance screening modality will reduce the number of people requiring further confirmation tests. Modified FINDRISC has the potential for use in daily clinical practice, and the cost-effectiveness of Modified FINDRISC should be further evaluated.

In the US, pre-diabetes and diabetes are increasing in prevalence alongside other chronic diseases. Hemoglobin A1c is the most common diagnostic test for diabetes performed in the US, but it has known inaccuracies in the setting of other chronic diseases.

To determine if easily obtained clinical data could be used to improve the diagnosis of pre-diabetes and diabetes compared to hemoglobin A1c alone.

This cross-sectional study analyzed nationally representative data obtained from six 2-year cycles (2005 to 2006 through 2015 to 2016) of the National Health and Nutrition Examination Survey in the US. We excluded participants without hemoglobin A1c, oral glucose tolerance test, or sample weight data. The sample comprised 13,800 survey participants. Data analyses were performed from May 1, 2024 to February 9, 2025.

We estimated 2-hour glucose from a gradient boosted machine decision tree machine learning model to diagnose pre-diabetes and diabetes as defined by oral glucose tolerance test 2-hour glucose of greater than or equal to 140 mg/dL but less than 200 mg/dL and greater than or equal to 200 mg/dL, respectively. We compared the area-under-the-receiver-operating-curve (AUROC), the calibration, positive predictive value, and the net benefit by decision curve analysis to hemoglobin A1C alone.

A 20-feature Model outperformed the hemoglobin A1c and fasting plasma glucose for diagnosis, with AUROC improvement from 0.66/0.71 to 0.77 for pre-diabetes and from 0.87/0.88 to 0.91 for diabetes. The Model also had improved positive predictive value compared to the A1c for diagnosis and for net benefit on decision curve analysis. Main features that improved diagnosis of pre-diabetes and diabetes were the standard vitals: age, height, weight, waist circumference, blood pressure, pulse, the fasting labs plasma glucose, insulin, triglycerides, and iron, the non-fasting labs cholesterol, gamma-glutamyl transferase, creatinine, platelet count, segmented neutrophil percentage, urine albumin, and urine creatinine, and the social determinant of health factor Poverty Ratio.

In this cross-sectional study of NHANES participants, we identified risk factors that could be incorporated into the electronic medical record to identify patients with potentially undiagnosed pre-diabetes and diabetes. Implementation could improve diagnosis and lead to earlier intervention on disease before it becomes severe and complications develop.

Question: Can readily-available clinical data improve diagnosis of pre-diabetes and diabetes compared to hemoglobin A1c testing alone?Findings: In this cross-sectional study of 13,800 adults with paired hemoglobin A1c and oral glucose tolerance testing in the National Health and Nutrition Examination Survey, the rate of pre-diabetes undiagnosed by 8.6% and rate of diabetes undiagnosed by the hemoglobin A1c was 3.5%. A novel multivariable prediction model that included fasting plasma glucose, insulin, basic body measurements, and routinely available dyslipidemia and hepatic function labs for was significantly more accurate (AUROC 0.66/0.71 to 0.77 for pre-diabetes, 0.87/0.88 to 0.91 for diabetes) than hemoglobin A1C or fasting plasma glucose alone.Meaning: Incorporation of easily obtainable clinical data can improve diagnosis of pre-diabetes and diabetes compared to hemoglobin A1C alone.

The quantification of coronary artery calcifications (CAC) is a mainstay in radiological assessment of coronary atherosclerosis and cardiovascular risk, but reflect advanced, possibly late-stage changes in the arteries. Increased volume and changes in attenuation of the epicardial adipose tissue (EAT) on computed tomography (CT) have been linked to adverse cardiovascular events, and these changes in the EAT might reflect earlier stages of the processes leading to clinically manifest atherosclerosis. The relationship between EAT and CAC is subject to a knowledge gap, especially in individuals with no previously known coronary artery disease.

Fully automated EAT analysis with an artificial intelligence-based model was performed in a population sample enriched for pre-diabetics, comprising a total of 1,945 individuals aged 50-64 years, where non-contrast CT images, anthropometric and laboratory data was available on established cardiovascular risk factors. Uni- and multivariable linear regression, gradient-boosting and correlation analyses were performed to determine the explanatory value of EAT volume and attenuation data with regards to CAC data.

Neither EAT volume nor EAT attenuation was associated with the presence or severity of CAC, when adjusting for established cardiovascular risk factors, and had only weak explanatory value in gradient-boosting and correlation analyses. Age was the strongest predictor of CAC in both sexes.

No independent association was found between CAC and total EAT volume or attenuation. Importantly, these findings do not rule out early stage or local effects on coronary atherosclerosis from the EAT immediately surrounding the coronary arteries.

Motivation for the study. There is an increase in obesity and diabetes mellitus cases in Cuba, so it is necessary to provide easy to use, fast and inexpensive tools for the identification of people with dysglycemia. Main findings. For the first time in CUBA, the optimal cut-off point for FINDRISC, LA-FINDRISC and modified FINDRISC for Cuba (CUBDRISC) questionnaires was established with its own anthropometric parameters to identify people with dysglycemia. Implications. The use of the CUBDRISC scale as a simple, fast and low-cost tool for the active screening of people with dysglycemia in Cuban population will be useful to establish timely intervention strategies for people with risk score to develop dysglycemia.

To evaluate the Finnish Diabetes Risk Score (FINDRISC) modified for Cuba as a tool for the detection of prediabetes and undiagnosed diabetes in Cuban population.

An analytical cross-sectional and secondary source epidemiological study was conducted in 3737 adults aged 19 years and older with at least one risk factor for diabetes, they did not have previous diagnosis of prediabetes and diabetes mellitus and underwent oral glucose tolerance test for the diagnosis of dysglycemia. We applied the FINDRISC and the FINDRISC modified for Latin America (LA-FINDRISC) and Cuba (CUBDRISC), each with their own anthropometric parameters. The ROC curve was used to establish the cut-off point of each scale for the diagnosis of dysglycemia. Sensitivity, specificity, predictive values and likelihood ratios were calculated. The concordance between scales was calculated with Cohen's Kappa coefficient.

We found that 34.5% (n=1289) of the subjects were diagnosed with dysglycemia (28.1% had prediabetes and 6.4% had type 2 diabetes without previous diagnosis). The LA-FINDRISC and CUBDRISC scales showed an almost perfect concordance with the FINDRISC scale for the different cut-off values from 11 to 16 (0.882-0.890 and 0.910-0.922, respectively). The optimal cutoff point for detecting persons with dysglycemia was ≥ 13 for the FINDRISC and CUBDRISC scales (sensitivity was 63.6% and 61.6%; specificity was 84.3% and 86.0%, respectively) and ≥11 for LA-FINDRISC (sensitivity 58.0% and specificity 88.0%).

We found almost perfect concordance between the diabetes risk scales. The FINDRISC score modified for Cuba proved to be a useful tool to identify persons with prediabetes and diabetes with a cut-off point of 13 in a Cuban population.

Many tools have been developed to predict the risk of diabetes in a population without diabetes; however, these tools have shortcomings that include the omission of race, inclusion of variables that are not readily available to patients, and low sensitivity or specificity.

We aimed to develop and validate an easy, systematic index for predicting diabetes risk in the Asian population.

We collected the data from the NAGALA (NAfld [nonalcoholic fatty liver disease] in the Gifu Area, Longitudinal Analysis) database. The least absolute shrinkage and selection operator model was used to select potentially relevant features. Multiple Cox proportional hazard analysis was used to develop a model based on the training set.

The final study population of 15464 participants had a mean age of 42 (range 18-79) years; 54.5% (8430) were men. The mean follow-up duration was 6.05 (SD 3.78) years. A total of 373 (2.41%) participants showed progression to diabetes during the follow-up period. Then, we established a novel parameter (the FHTHWA index), to evaluate the incidence of diabetes in a population without diabetes, comprising 6 parameters based on the training set. After multivariable adjustment, individuals in tertile 3 had a significantly higher rate of diabetes compared with those in tertile 1 (hazard ratio 32.141, 95% CI 11.545-89.476). Time receiver operating characteristic curve analyses showed that the FHTHWA index had high accuracy, with the area under the curve value being around 0.9 during the more than 12 years of follow-up.

This research successfully developed a diabetes risk assessment index tailored for the Japanese population by utilizing an extensive dataset and a wide range of indices. By categorizing the diabetes risk levels among Japanese individuals, this study offers a novel predictive tool for identifying potential patients, while also delivering valuable insights into diabetes prevention strategies for the healthy Japanese populace.

To examine recent economic evaluations and understand whether any type 2 diabetes mellitus (T2DM) screening designs may represent better value for money and to rate their methodological qualities.

We systematically searched 3 concepts (economic evaluations [EEs], T2DM, screening) in 5 databases (Medline, Embase, EconLit, Web of Science, and Cochrane) for EEs published between 2010 and 2023. Two independent reviewers screened for and rated their methodological quality (using the Consensus on Health Economics Criteria Checklist-Extended).

Of 32 EEs, a majority were from high-income countries (69%). Half used single biomarkers (50%) to screen adults ≥30 to <60 years old (60%) but did not report locations (69%), treatments for those diagnosed (66%), diagnostic methods (57%), or screening intervals (54%). Compared with no screening, T2DM screening using single biomarkers was found to be not cost-effective (23/54 comparisons), inconclusive (16/54), dominant (11/54), or cost-effective (4/54). Compared with no screening, screening with a risk score and single biomarkers was found to be cost-effective (21/40) or dominant (19/40). The risk score alone was mostly dominant (6/10). Compared with universal screening, targeted screening among obese, overweight, or older people may be cost-effective or dominant. Compared with fasting plasma glucose or fasting capillary glucose, screening using risk scores was found to be mostly dominant or cost-effective. Expanding screening locations or lowering HbA1c or fasting plasma glucose thresholds was found to be dominant or cost-effective. Each EE had 4 to 17 items (median 13/20) on Consensus on Health Economics Criteria Checklist-Extended rated "Yes/Rather Yes."

EE findings varied based on screening tools, intervals, locations, minimum screening age, diagnostic methods, and treatment. Future EEs should more comprehensively report screening designs and evaluate T2DM screening in low-income countries.

The aim of this study was to investigate the difference between perceived and calculated diabetes risks among post-myocardial infarction (AMI) patients using the Finnish Diabetes Risk Score (FINDRISC).

The study population includes individuals from the Myocardial Infarction Registry in Augsburg, Germany, who had not been previously diagnosed with diabetes and who received a postal follow-up questionnaire after hospital discharge. A total of 466 participants completed the questionnaire, which collected information on age, sex, body mass index (BMI), waist circumference, physical activity, eating habits, use of antihypertensive medication, previous hyperglycemia, and family history of diabetes. These factors are components of the FINDRISC score, which estimates the likelihood of developing diabetes within the next 10 years. Furthermore, the participants were asked, how they would rate their personal risk to develop diabetes. The analysis focused on determining how many post-AMI patients correctly estimated their diabetes risk compared to the risk calculated by the FINDRISC score. Furthermore, multivariable logistic regression was used to analyze determinants associated with risk underestimation.

Results showed that a significant proportion of the AMI population (58%) underestimated their diabetes risk. This underestimation was significantly associated with older age, higher BMI, greater waist circumference, elevated blood glucose levels, use of antihypertensive medication and a family history of diabetes. Higher education contributed to more accurate risk perception.

This study contributes to the understanding of diabetes risk perception in AMI patients and highlights the need for improving diabetes risk awareness through targeted education and healthcare communication interventions. These efforts can help patients understand their health risks, which improves health outcomes and preventive care.

Vascular cognitive impairment (VCI) stresses the vascular contributions to cognitive decline, ranging from mild to major forms. Except for symptomatic treatment for relevant vascular diseases, the other recommended strategy is to intervene in key vascular risk factors (VRFs) as early as possible. A considerable amount of previous research delineated the association of a specific factor with dementia, involving each risk factor discussed in the present review. However, due to the heterogeneity and complexity of VCI, managing a single factor is insufficient to reduce its incidence and prevalence. Ongoing studies suggest differences in the impact of various combinations of risk factors on dementia. Here in this review, we aimed to provide an updated overview of clinical evidence and implications of complex interactions among various risk factors of VCI, including common VRFs and modifiable dementia-related risk factors. Understating the effect of comorbid risk factors on VCI and underlying mechanisms of them during VCI progression is essential for identifying high-risk population and developing preventive strategies. Furthermore, we summarized common composite risk scores and models used for risk evaluation and prediction of VCI, involving conventional risk scores, subclinical vascular composites, and novel risk models driven by intelligent algorithms. Lastly, we discussed potential gaps and research directions on searching specific clinical risk profiles, constructing effective risk scores, and implementing targeted risk interventions.

The COVID-19 pandemic exposed a global deficiency of systematic, data-driven guidance to identify high-risk individuals. Here, we illustrate the utility of routinely recorded medical history to predict the risk for 1741 diseases across clinical specialties and support the rapid response to emerging health threats such as COVID-19. We developed a neural network to learn from health records of 502,489 UK Biobank participants. Importantly, we observed discriminative improvements over basic demographic predictors for 1546 (88.8%) endpoints. After transferring the unmodified risk models to the All of US cohort, we replicated these improvements for 1115 (78.9%) of 1414 investigated endpoints, demonstrating generalizability across healthcare systems and historically underrepresented groups. Ultimately, we showed how this approach could have been used to identify individuals vulnerable to severe COVID-19. Our study demonstrates the potential of medical history to support guidance for emerging pandemics by systematically estimating risk for thousands of diseases at once at minimal cost.

We have shown that men aged 50 years+ at high risk of type 2 diabetes treated with testosterone together with a lifestyle program reduced the risk of type 2 diabetes at 2 years by 40% compared to a lifestyle program alone. To develop a personalized approach to treatment, we aimed to explore a prognostic model for incident type 2 diabetes at 2 years and investigate biomarkers predictive of the testosterone effect.

Model development in 783 men with impaired glucose tolerance but not type 2 diabetes from Testosterone for Prevention of Type 2 Diabetes; a multicenter, 2-year trial of Testosterone vs placebo. External validation performed in 236 men from the Examining Outcomes in Chronic Disease in the 45 and Up Study (EXTEND-45, n = 267 357).

Type 2 diabetes at 2 years defined as 2-h fasting glucose by oral glucose tolerance test (OGTT) ≥11.1 mmol/L. Risk factors, including predictive biomarkers of testosterone treatment, were assessed using penalized logistic regression.

Baseline HbA1c and 2-h OGTT glucose were dominant predictors, together with testosterone, age, and an interaction between testosterone and HbA1c (P = .035, greater benefit with HbA1c ≥ 5.6%, 38 mmol/mol). The final model identified men who developed type 2 diabetes, with C-statistics 0.827 in development and 0.798 in validation. After recalibration, the model accurately predicted a participant's absolute risk of type 2 diabetes.

Baseline HbA1c and 2-h OGTT glucose predict incident type 2 diabetes at 2 years in high-risk men, with risk modified independently by testosterone treatment. Men with HbA1c ≥ 5.6% (38 mmol/mol) benefit most from testosterone treatment, beyond a lifestyle program.

Identifying the risks of metabolic syndrome (MetS) can lead to early targeted interventions and thus contribute to improved quality of life by reducing the risk of developing MetS, diabetes or heart disease in the future. We aimed to develop a valid and reliable measurement tool to measure the MetS risk of the population.

In the methodological study, an item pool was created by reviewing the literature. Pre-application was performed after the weighting of the items whose content validity was ensured by taking expert opinions. Data were collected from 43 patients with MetS from a state hospital affiliated to the Ministry of Health and 405 individuals without MetS from the community, from a total of 448 individuals using the Individual Information Form, Finnish Diabetes Risk Scale (FINDRISC) and Metabolic Syndrome Index (MSI). The data obtained were evaluated using SPSS 22.0 and MedCalc 19.1 statistical programmes. Scale discrimination was analyzed by independent samples t-test between the upper and lower 27% groups. The cut-off point of the scale score in predicting the diagnosis of MetS was tested by ROC analysis. Correlation analysis was performed with the parallel form for criterion validity.

As a result of the ROC analysis, a perfectly compatible scale with a sensitivity of 100%, a specificity of 85.43% and a cut-off score of 48 was obtained. When the correlation analyses between MSI and FINDRISC scores were examined for criterion validity, a positive moderate (r = 0.632, p < 0.001) correlation was found between FINDRISC and MSI. When the discrimination of the scale was analysed, it was found that there was a significant difference between the lower 27% and upper 27% groups (p < 0.05) and it was revealed that the MSI made sensitive measurements to discriminate.

The MSI scale is a valid and reliable tool for early detection of MetS risk.

Obesity and certain associated environmental factors increase the risk of type 2 diabetes mellitus (T2DM). This research aims to evaluate the relationship between sociodemographic characteristics, lifestyle, and diet quality with diabetes risk in overweight and obese Turkish adults.

A questionnaire form including sociodemographic characteristics, lifestyle, body weight and height was applied. Finnish Diabetes Risk Score (FINDRISC) tool was used to identify the risk of T2DM. Dietary assessments were made by 24 h dietary recall and diet quality was evaluated by Healthy Eating Index-2015 (HEI-2015).

According to FINDRISC score, 38.1% of adults were at mild risk, 21.9% were at moderate risk, and 20.9% were at high risk. In regression model, factors such as low educational level, being married, being employed, smoking, and the presence of comorbidities were found to increase the risk of developing diabetes. Each unit decline in HEI-2015, the risk of diabetes increased by a factor of 0.983.

Sociodemographic factors, lifestyle, and diet quality significantly contribute to the increased risk of diabetes in overweight and obese Turkish adults. This trial was registered at clinicaltrials.gov with number NCT06614075 and registration date of 26 September 2024, retrospectively registered.

Although post-transplant diabetes mellitus (PTDM) is a common complication after kidney transplantation, there are few data on prevention, optimal screening, and treatment strategies.

The European Renal Association's DESCARTES working group distributed a web-based survey to European transplant centres to gather information on risk assessment, screening procedures, and management practices for preventing and treating PTDM in kidney transplant recipients.

Answers were obtained from 121/241 transplant centres (50%) across 15 European countries. Screening practices for diabetes mellitus during the transplant work-up varied, with only 13% of centres using the recommended oral glucose tolerance test (OGTT) and 14% not screening at all. At transplantation, 19% of centres tailored the immunosuppressive regimen based on perceived PTDM risk, using strategies such as cyclosporin use or early steroid withdrawal. Fifty-two percent adopted strict glycaemic control with basal insulin in the first days post-transplant. Sixty-eight percent had defined screening protocols for early PTDM (45 days-6 months), primarily based on fasting glycaemia and/or HbA1c, while only a minority (7%) incorporated an OGTT. Changes in immunosuppression were considered by 41% in cases of early hyperglycaemia (<45 days) and by 58% in established PTDM (>45 days). Besides insulin therapy, dipeptidyl peptidase-4 (DPP4) inhibitors and metformin were most frequently used to manage early hyperglycaemia (<45 days) and PTDM (>45 days). The use of SGLT2 inhibitors and GLP-analogues increased >45 days post-transplantation.

This European survey underscores the significant variation in PTDM prevention, screening, and treatment practices, emphasizing the imperative for more explicit guidance in approaching this complication.