Diabetes Risk Factors Research Articles

Pancreatic beta cell function and insulin resistance profiles in first-degree relatives of patients with prediabetes and type 2 diabetes.

To evaluate insulin secretion and insulin resistance profiles in individuals with family history of prediabetes and type 2 diabetes. This was a cross-sectional study to evaluate clinical and metabolic profiles between individuals with type 2 diabetes, prediabetes and their relatives. There were 911 subjects divided into five groups: (i) normoglycemic (NG), (ii) type 2 diabetes, (iii) prediabetes, (iv) first-degree relatives of patients with type 2 diabetes (famT2D), and (v) first-degree relatives of patients with prediabetes (famPD); anthropometrical, biochemical and nutritional evaluation, as well as insulin resistance and pancreatic beta cell function measurement was performed by oral glucose tolerance to compare between groups. The most prevalent type 2 diabetes risk factors were dyslipidemia (81%), family history of type 2 diabetes (76%), central obesity (73%), male sex (63%), and sedentary lifestyle (60%), and most of them were progressively associated to prediabetes and type 2 diabetes groups. Insulin sensitivity was lower in famT2D groups in comparison to NG group (p < 0.0001). FamPD and famT2D had a 10% lower pancreatic beta cell function (DI) than the NG group (NG group 2.78 ± 1.0, famPD 2.5 ± 0.85, famT2D 2.4 ± 0.75, p˂0.001). FamPD and famT2D patients had lower pancreatic beta cell function than NG patients, highlighting that defects in insulin secretion and insulin sensitivity appear long time before the development of hyperglycemia in patients genetically predisposed.

Machine Learning Analysis of Health and Lifestyle Factors in Understanding Diabetes

Diabetes poses a significant global health challenge, with approximately 537 million adults living with the condition in 2021, a number expected to rise to 783 million by 2045. To enhance predictive accuracy and gain deeper insights into the factors contributing to diabetes, this study employed machine learning algorithms to predict diabetes risk factors using a dataset encompassing health and lifestyle variables. Six supervised machine learning algorithms, including Gradient Boosting, Logistic Regression, and Random Forest, among others, were assessed for their effectiveness in classifying diabetes status into two categories: diabetes and no diabetes. The study found that Gradient Boosting achieved the highest overall accuracy at 85%, demonstrating the best recall for diabetic cases at 57%. Meanwhile, Logistic Regression excelled in precision for non-diabetic cases at 94%. Key risk factors identified include general health status, blood pressure, body mass index, cholesterol levels, and age. Notably, the study uncovered that higher income and education levels were associated with increased diabetes risk, contradicting some existing literature and indicating the potential impact of lifestyle factors.

Exploration of predictive risk factors for diabetic foot in patients with diabetes in Beijing: analysis of 5-year follow-up data of patients with diabetes mellitus in a single center in Beijing.

Large-scale prospective cohort studies on diabetic foot ulcers risk factor screening in China are limited. Therefore, this prospective cohort study aimed to explore the predictive risk factors for diabetic foot ulcers to provide clinicians with concise and effective clinical indicators for identifying a high-risk diabetic foot and guiding the prevention of diabetic foot ulcers. Patients with diabetes who visited the Department of Endocrinology of Peking University First Hospital from October 2017 to December 2018 were selected as research participants by convenience sampling. A total of 968 patients were included. After enrollment, a dedicated person collected and recorded all baseline data. A dedicated telephone follow-up was conducted every 12-24 months to evaluate whether the endpoint event had occurred. All patients were followed up for an average of 61 (57-71) months, with 95% of them followed up for more than 60 months. According to the occurrence of endpoint events, they were divided into the DFU and non-DFU groups. The data between the two groups were analyzed using independent-sample t-test, Wilcoxon rank sum test, and chi square test. We used univariate and multivariate logistic regression analysis to analyze the factors that affected the occurrence of diabetic foot ulcers. After the 5-year follow-up, the incidence of diabetic foot was 25.83%. Multivariate logistic regression analysis revealed that body mass index (odds ratio: 1.046; 95% confidence interval: 1.001-1.093), abnormal pinprick sensation (odds ratio: 4.138; 95% confidence interval: 1.292-13.255), history of fungal foot infection (odds ratio: 2.287; 95% confidence interval: 1.517-3.448), abnormal 128-Hz tuning fork test (odds ratio: 2.628; 95% confidence interval: 1.098-6.294), and HbA1c≥ 8% (odds ratio: 1.522; 95% confidence interval: 1.014-2.284) were independent predictors of diabetic foot. Our study highlights clinically relevant indicators that may help to prevent the occurrence of diabetic foot and guide timely interventions.

A Systematic Review of Existing Data on Statin Use and New-Onset Type 2 Diabetes: The Need for Clear Answers

Background: The relationship between statins use and the development of new-onset type 2 diabetes mellitus (NOT2DM) has been a subject of ongoing debate and investigation. In February 2012, the Food and Drug Administration (FDA) disclosed that statin use could lead to a modest rise in glycosylated hemoglobin (HbA1c) and fasting blood glucose (FBG), potentially contributing to the development of diabetes. We systematically reviewed the available evidence and summarized the impact of statin use on NOT2DM. Aim: This systematic review aims to examine and provide a comprehensive overview of existing data relating to statin use and their association with the development of new-onset type 2 diabetes (NOT2DM). The primary objectives were to assess and explore: (1) the potential association between statin use and the development of new-onset diabetes; (2) the magnitude, extent, and incidence of new-onset diabetes due to statin use. The secondary objective was to explore the underlying mechanisms. Data Sources: PubMed, Scopus, Science Direct, Wiley, and Google Scholar and databases between January 2012 and February 2021. Data Extraction: The 2 reviewers Musa Basheer Mansour and Sara Elsheikh Ahmedana [MBM and SEA] independently assessed the qualities of the extracted studies and summarized data of the selected studies in tabulated forms for outcomes of interest and performed methodological and quality assessments based on review questions, citations, country of the study, aim, population characteristics, design, setting, sample size, sample technique, data source, measures, analysis, confounder variable and key observations for systematic review. We applied search keywords such as Statins, New-onset diabetes, Diabetes mellitus, Underlying mechanism, and incidents of diabetes. Results: A total of 66 studies were identified through database searches in the initial search, and 7 studies were included in this review. Of the 2,567,888 adult patients, 861,925 were nondiabetic patients on statin use, and 1,705,963 were diabetic on statin or non-statin users. We considered Hazard Ratios (HR), Odds Ratios (OR), and Confidence Intervals (CI) of each study for further analysis. The mean OR for three studies showed that each statin therapy resulted in a 68% increased risk of NOT2DM (OR, 1.683; 95% CI: 1.273-2.237). The significant relationship between statin use and the rising risk of NOT2DM was also confirmed by an average HR of 1.53 (95% CI: 1.2-1.7) for the remaining four studies. Recent and short-term use of statins, as well as time- and dose-dependent connections, were linked to an increased risk of NOT2DM in statin users compared to non-users. These risks were more prominent among older adults, normotensive males, hypertensive females, and individuals with low physical activity. Although no proven mechanisms were found, the possible processes are discussed. Conclusion: The systematic review found a significant level of risk of NOT2DM associated with statin use. The authors argued that the short-term diabetes risk caused by statin treatment was more prevalent among patients exposed to risk factors for diabetes. Although the precise mechanisms behind this association remain unclear, this research provides clinical practice with evidence-based guidelines. Additionally, conducting future research may be appropriate based on the gaps in knowledge identified from the results of this review.

Engaging Students in a Bilingual Service-Learning Health Promotion Project.

Introduction: This service-learning, health promotion project used Leininger's Culture Care Theory to engage Baccalaureate Nursing students in a health promotion initiative specific to Hispanic community members. Methods: Thirty-seven students examined population health data, surveyed Hispanic community members to identify health priorities, and developed a population-specific intervention on diet and risk factors for diabetes. Students created a 5-min script focused on healthy eating plans with recognition of traditional foods associated with the Hispanic culture. Students delivered the script to community members attending an annual Hispanic Heritage Festival. Results: Seventy-six percent of the 488 community participants reported increased knowledge about diabetes risk factors and healthy eating habits. Each student completed a self-reflection on how the project impacted their transcultural skills and reported increased confidence related to engagement and communication with the Hispanic community. Discussion/Significance: Incorporating community-based service-learning into the undergraduate curricula is a powerful educational approach that can enhance students' ability to provide culturally congruent nursing care.

Association between cardiovascular health and serum vitamin D and its interaction with prediabetes and diabetes

BackgroundPrediabetes and diabetes are common and serious public health problems, and high blood glucose can lead to serious cardiovascular complications. The purpose of this article was to explore the link between CVH levels and the incidence of prediabetes and diabetes in people over 20 years old, and whether serum vitamin D status could alter this relationship. Materials and MethodsData, from six consecutive cycles of the NHANES database from 2007 to 2018 were combined, eligible participants were aged ≥20 years. After excluding missing data, a total of 19,992 subjects were enrolled in the study. Significant risk factors for prediabetes and diabetes were analyzed using univariate and multivariate logistic regression. Exploring the interaction of VD and CVH on prediabetes and diabetes based on multifactorial regression analysis. ResultsThe prevalence of prediabetes among all participants was 36.15% and the prevalence of diabetes was 16.39%. CVH and vitamin D levels are influential factors in prediabetes and diabetes, and are negatively associated with the risk of developing prediabetes and diabetes. Compared with normoglycemia, poorer CVH and vitamin D deficiency only had a synergistic multiplicative interaction on the development of diabetes, and no significant interaction was observed for the development of prediabetes. Compared with prediabetes, poorer CVH and vitamin D deficiency still had a synergistic additive interaction on the development of diabetes. ConclusionsAlthough the cross-sectional study only determine the association and do not prove causality, the current results can be used to prompt people to improve their lifestyle and risk factors to prevent prediabetes or diabetes through higher CVH and adequate Vitamin D.

Edukasi dan Promosi Kesehatan Diabetes Melitus Lingkungan XIX Kelurahan Tanjung Mulia Hilir Kecamatan Medan Deli

Education and health care for diabetes mellitus in Environment Objective: This implementation is to increase knowledge and health care about diabetes mellitus in Environment XIX, Tanjung Mulia Hilir Village, Medan Deli District. Health promotion for diabetes mellitus (DM) focuses on empowering individuals and communities to make sustainable lifestyle changes that prevent or delay the onset of type 2 diabetes and effectively manage existing diabetes to minimize complications. This method involves a multifaceted approach that includes: 1). Prevention, namely increasing awareness by educating the public about diabetes risk factors (such as obesity, lack of physical activity, unhealthy eating patterns, family history) and promoting healthy lifestyle choices. Encourage regular screening for early diagnosis, especially for those with risk factors. Promote healthy eating habits, regular physical activity, and maintaining a healthy weight. Expected Outcome: increasing public awareness of diabetes risk factors (such as obesity, lack of physical activity, unhealthy diet, family history) and promoting healthy lifestyle choices. As well as the existence of guidelines for healthy eating and physical activity. Mechanisms for solving problems and overcoming them.

The relationship of knowledge about Diabetes Mellitus risk factors with the healthy lifestyle among nursing students

Background: The incidence of Type 2 Diabetes Mellitus (T2DM) in young age groups continues to increase. Student knowledge about diabetes risk factors is still in the category of lacking so that students cannot apply healthy lifestyle implementation behaviors correctly. Purpose: The study aims to determine the relationship between knowledge and healthy living behavior in students of the Faculty of Nursing, Andalas University. Methods: The design of this study was cross sectional. The sample number was 218 students of the Faculty of Nursing and used the propotional random sampling technique. The research data was collected with DM risk factor knowledge questionnaire and healthy lifestyle application questionnaire through Google form media. Data analysis using chi-square test. Result: Researchers found that there was a relationship between knowledge of DM risk factors and the application of a healthy lifestyle (p = 0.002), where knowledge about DM risk factors was obtained in the sufficient category with the application of a low healthy lifestyle. Conclusion: Therefore, it is still necessary to increase student knowledge about diabetes mellitus risk factors.

Posterior Circulation Ischemic Stroke Is Associated with More Severe Forward Head Posture in Patients with Cervicalgia

Chronic risk factors of posterior circulation ischemic strokes remain unknown. There is, however, evidence that anatomical variations within the vertebrobasilar system could potentially lead to the development of posterior circulation strokes. We investigated the association of forward head posturing (FHP), a chronic anatomical change seen in patients with cervicalgia, with posterior circulation strokes. In this case-control study, we identified adult patients diagnosed with cervicalgia and ischemic posterior circulation infarcts from 2007 to 2023. Cases were paired with controls with cervicalgia without posterior circulation stroke and matched for known risk factors. C0-C2 angle, C2-C7 angle, and sagittal vertical axis shift were measured as metrics for FHP severity on lateral cervical spine x-rays. Paired t tests were applied to compare stroke risk factors and FHP between groups. Participants included 206 patients (103 cases and 103 controls) with a mean age of 64.4 years; 51.5% were female. There were no statistically significant differences between risk factors for stroke, including hypertension, hyperlipidemia, diabetes, smoking, hypercoagulable state, and atrial fibrillation. There were statistically significant differences in C0-C2 angle (30.7° vs. 23.2°, P=0.002) and sagittal vertical axis shift (35.4 mm vs. 25.3 mm, P < 0.001). This case-control matched-pair study found that in adults with cervicalgia, posterior circulation ischemic stroke was associated with more severe FHP as noted in C0-C2 angle and sagittal vertical axis shift measurements. Further, this association may be due to chronic changes in circulation flow dynamics within the vertebrobasilar system with the postural change.

Enhanced Diabetes Prediction and Monitoring through Integration of Multimodal Data Using XGBoost

Abstract: Diabetes is a critical and become more complicated disease that can cause serious health problems if it is not adequately managed. The early diagnosis and treatment of diabetes is a critical component of the condition that can be greatly aided by data analysis and predictive algorithms. Through the use of data mining techniques, such as classification and prediction models, it is possible to analyse various elements of diabetes data and extract useful information that can be used for the early detection and prediction of the condition. One machine learning technique that can effectively and highly precisely predict diabetes is the XGBoost classifier. This method makes use of the gradient-boosting architecture and can handle large and intricate datasets with independent high-dimensional feature sets. Conversely, it is crucial to remember that the choice of the best algorithm for diabetes prediction could depend on the specifics of the data as well as the area of study being investigated. Data analysis and prediction methods can be applied not only to anticipate diabetes but also to monitor the disease's progression, find risk factors for diabetes and its complications, and assess the effectiveness of treatment. By using these techniques, medical professionals can obtain important insights into the disease's underlying causes, which helps them make informed decisions about patient management. The early detection and management of diabetes, a chronic disease that is rapidly expanding and poses major health risks, has the potential to be significantly improved through the application of data analysis and prediction algorithms. An accuracy rate of 89% was achieved by the XGBoost classifier, which demonstrated the highest level of performance.

A Systematic Review of Existing Data on Statin Use and New - Onset Type 2 Diabetes: The Need for Clear Answers

Background: The relationship between statins use and the development of new-onset type 2 diabetes mellitus (NOT2DM) has been a subject of ongoing debate and investigation. In February 2012, the Food and Drug Administration (FDA) disclosed that statin use could lead to a modest rise in glycosylated hemoglobin (HbA1c) and fasting blood glucose (FBG), potentially contributing to the development of diabetes. We systematically reviewed the available evidence and summarized the impact of statin use on NOT2DM. Aims and Objectives: This systematic review aims to examine and provide a comprehensive overview of existing data relating to statin use and their association with the development of new-onset type 2 diabetes (NOT2DM). The primary objectives were to assess and explore: (1) the potential association between statin use and the development of new-onset diabetes; (2) the magnitude, extent, and incidence of new-onset diabetes due to statin use. The secondary objective was to explore the underlying mechanisms. Data Sources: PubMed, Scopus, Science Direct, Wiley, and Google Scholar and databases between January 2012 and February 2021. Data Extraction: The 2 reviewers Musa Basheer Mansour and Sara Elsheikh Ahmedana [MBM and SEA] independently assessed the qualities of the extracted studies and summarized data of the selected studies in tabulated forms for outcomes of interest and performed methodological and quality assessments based on review questions, citations, country of the study, aim, population characteristics, design, setting, sample size, sample technique, data source, measures, analysis, confounder variable and key observations for systematic review. We applied search keywords such as Statins, New-onset diabetes, Diabetes mellitus, Underlying mechanism, and incidents of diabetes. Results: A total of 66 studies were identified through database searches in the initial search, and 7 studies were included in this review. Of the 2,567,888 adult patients, 861,925 were nondiabetic patients on statin use, and 1,705,963 were diabetic on statin or non-statin users. We considered Hazard Ratios (HR), Odds Ratios (OR), and Confidence Intervals (CI) of each study for further analysis. The mean OR for three studies showed that each statin therapy resulted in a 68% increased risk of NOT2DM (OR, 1.683; 95% CI: 1.273-2.237). The significant relationship between statin use and the rising risk of NOT2DM was also confirmed by an average HR of 1.53 (95% CI: 1.2-1.7) for the remaining four studies. Recent and short-term use of statins, as well as time- and dose-dependent connections, were linked to an increased risk of NOT2DM in statin users compared to non-users. These risks were more prominent among older adults, normotensive males, hypertensive females, and individuals with low physical activity. Although no proven mechanisms were found, the possible processes are discussed. Conclusion: The systematic review found a significant level of risk of NOT2DM associated with statin use. The authors argued that the short-term diabetes risk caused by statin treatment was more prevalent among patients exposed to risk factors for diabetes. Although the precise mechanisms behind this association remain unclear, this research provides clinical practice with evidence-based guidelines. Additionally, conducting future research may be appropriate based on the gaps in knowledge identified from the results of this review.

Beneficial effects of extra virgin olive oil on type 2 diabetes

In contemporary society, chronic diseases become increasingly prevalent, among which is type 2 diabetes. Nowadays, daily diet is gaining growing attention and interest from the general public for its potential to prevent and improve these diseases. One of the ingredients highly recommended to be included in peoples diet is extra virgin olive oil (EVOO), since substantial evidence has shown that it is helpful in reducing body weight and the risk of obesity and type 2 diabetes. Consequently, this review paper is aimed at comparing EVOO with other types of oil or low-fat diets and summarizing its health benefits to type 2 diabetes by analyzing relevant parameters, including fasting blood glucose (FBG), insulin, glycated hemoglobin (HbA1c), body weight, Homeostatic Model Assessment for Insulin Resistance (HOMA-IR), etc. A systematic literature search was conducted using Google Scholar with the year range from 2010 to 2023. After being considered against the exclusion criteria, 8 articles were eventually included. Results showed that EVOO is associated with improving some risk factors of diabetes, leading to decreased blood glucose and greater short-term weight loss compared to low-fat diets, and resulting in more body fat loss compared to soybean oil. Thus, EVOO is now expected to be clinically used in treating type 2 diabetes in the future.

Adult patients with Laron syndrome tend to develop the metabolic syndrome

BackgroundThe metabolic Syndrome is the name of a cluster of abnormal clinical and metabolic states, which constitute a risk factor for diabetes and cardiovascular disease. AimTo determine whether adult patients with Laron Syndrome with excessive obesity develop the characteristics of the Metabolic Syndrome. SubjectsOut of a cohort of adult patients with Laron Syndrome followed in our clinic, records of 23 patients (12 females, 11 males) were found to have sufficient data for analysis. MethodsThe degree of obesity was determined by the measurement of subscapular skinfold thickness (SSFT), BMI and total body DEXA. NAFLD was determined by liver ultrasonography, serum lipids including adiponectin leptin, insulin and glucose were assessed by radioimmunoassay. ResultsBoth female and male patients were markedly obese with 59% and 39% fat of the total body mass respectively, as were total and LDL cholesterol, triglycerides and adiponectin. Some had developed NAFLD. They also suffered from insulin resistance and glucose intolerance. Eleven patients (3 females, 8 males) developed diabetes. All had varying degrees of hypertension. Eight subjects (3 females, 5 males) suffered from cardiovascular disease. One female died at aged 53 years, and two males died at ages 75 and 78 years. ConclusionWith advancing age and increasing obesity, adult patients with Laron Syndrome developed the characteristics of Metabolic Syndrome including diabetes and cardiovascular disease.

Personalized Nutrition Therapy without Weight Loss Counseling Produces Weight Loss in Individuals with Prediabetes Who Are Overweight/Obese: A Randomized Controlled Trial.

Obesity stands out as a primary risk factor for diabetes. Attaining healthy weight loss, especially reducing body fat, is important in managing prediabetes and preventing progression to full diabetes and its co-morbidities. This study examined the effects of personalized nutrition therapy (PNT) combined with continuous glucose monitoring (CGM) on body weight and composition in individuals with prediabetes. A total of 30 individuals with prediabetes who were overweight or obese were assigned randomly to either the treatment, observed CGM data plus PNT, or the control group which was blinded to their blood glucose results throughout the study. Both groups were provided with dietary recommendations for calorie intake and macronutrient distribution, coupled with personalized goal setting for glucose control and healthy eating, without any specific emphasis on weight reduction or changes in physical activity. Regular visits were scheduled every 10 days to perform measurements and replace CGMs. Data were analyzed using General Linear Model with repeated measures. Over the 30-day follow-up period, both groups experienced significant reductions in weight and fat mass. The treatment group exhibited two-fold greater reductions in both weight and fat mass, a significant decrease in carbohydrate intake, and a significant increase in time spent on physical activitycompared to the control group. In addition, compliance was notably higher in the treatment group. These findings indicate that overweight or obese individuals with prediabetes can achieve weight loss and improved body composition through personalized education for glucose control, without exclusively emphasizing weight loss as the primary objective. Additionally, the real-time feedback provided by CGM enhances these improvements.

Anthropometric indicators and cardiovascular diseases risk in pre-diabetic and diabetic adults: NHANES 1999–2018 cross-sectional analysis

BackgroundSince cardiovascular disease (CVD) stands as the primary cause of death in those with diabetes, and given the substantial influence of obesity as a common risk factor for both diabetes and atherosclerotic conditions, this investigation sought to find the relationship between anthropometric indicators and CVD risk within these populations. MethodsOur study examined 36,329 adults, including those with diagnosed diabetes, pre-diabetes, and without diabetes from National Health and Nutrition Examination Survey (NHANES) data spanning 1999 to 2018. Various anthropometric indicators such as body mass index (BMI), waist circumference, weight-adjusted waist index (WWI), waist-to-height ratio (WHtR), weight, and height were assessed. Baseline characteristics were compared among the three groups after weighting. Participants were then grouped based on anthropometric indicators, and logistic regression models were used to analyze the association between these indicators and CVD risk in the total diabetes group (including diabetic and pre-diabetic individuals). Threshold effect analysis was conducted to explore nonlinear relationships, and mediation analyses assessed whether serum parameters influenced these relationships. ResultsThis cross-sectional study involved 36,329 participants, weighted to a count of approximately 160.9 million, including over 45.9 million pre-diabetic individuals and around 16.6 million diabetic individuals. Baseline analysis showed significant associations between all six anthropometric indicators and CVD risk across patients with different diabetes statuses. Weighted restricted cubic spline (RCS) curve analysis highlighted increased CVD risk among the total diabetes group for each anthropometric indicator compared to the non-diabetic group. Anthropometric indicators were then divided into quartiles, and after adjusting for confounders, Model 3 revealed that the highest BMI group had a heightened risk of CVD compared to the lowest BMI group. Similar trends were observed in the WWI and WHtR subgroups. Threshold effect analysis of anthropometric indicators unveiled nonlinear associations between waist circumference, height, WWI and CVD risk. Mediation analysis suggested that lipid parameters, especially HDL, significantly mediated these relationships. ConclusionIn individuals with diabetes and pre-diabetes, BMI, weight, and WHtR displayed a consistent, linear increase correlation with CVD risk. Conversely, the link between waist circumference, height, and WWI and CVD risk showcased a more complex, nonlinear pattern. Moreover, HDL level emerged as notable mediator in the association between anthropometric indicators and the risk of CVD.

Trends of diabetes in Beijing, China.

The global rise in diabetes prevalence is a pressing concern. Despite initiatives like "The Healthy Beijing Action 2020-2030" advocating for increased awareness, treatment, and control, the specific situation in Beijing remains unexplored. This study aimed to analyze the trends in diabetes prevalence, awareness, treatment, and control among Beijing adults. Through a stratified multistage probability cluster sampling method, a series of representative cross-sectional surveys were conducted in Beijing from 2005 to 2022, targeting adults aged 18-79 years. A face-to-face questionnaire, along with body measurements and laboratory tests, were administered to 111,943 participants. Data from all survey were age- and/or gender-standardized based on the 2020 Beijing census population. Annual percentage rate change (APC) or average annual percentage rate change (AAPC) was calculated to determine prevalence trends over time. Complex sampling logistic regression models were employed to explore the relationship between various characteristics and diabetes. From 2005 to 2022, the total prevalence of diabetes among Beijing adults aged 18-79 years increased from 9.6% (95% CI: 8.8-10.4%) to 13.9% (95% CI: 13.1-14.7%), with an APC/AAPC of 2.1% (95% CI: 1.1-3.2%, P<0.05). Significant increases were observed among adults aged 18-39 years and rural residents. Undiagnosed diabetes rose from 3.5% (95% CI: 3.2-4.0%) to 7.2% (95% CI: 6.6-7.9%) with an APC/AAPC of 4.1% (95% CI: 0.5-7.3%, P<0.05). However, diabetes awareness and treatment rates showed annual declines of 1.4% (95% CI: -3.0% to -0.2%, P<0.05) and 1.3% (95% CI: -2.6% to -0.2%, P<0.05), respectively. The diabetes control rate decreased from 21.5% to 19.1%, although not statistically significant (APC/AAPC=-1.5%, 95% CI: -5.6% to 1.9%). Overweight and obesity were identified as risk factors for diabetes, with ORs of 1.65 (95% CI: 1.38-1.98) and 2.48 (95% CI: 2.07-2.99), respectively. The prevalence of diabetes in Beijing has significantly increased between 2005 and 2022, particularly among young adults and rural residents. Meanwhile, there has been a concerning decrease in diabetes awareness and treatment rates, while control rates have remained stagnant. Regular blood glucose testing, especially among adults aged 18-59 years, should be warranted. Furthermore, being male, elderly, overweight, or obese was associated with higher diabetes risk, suggesting the needs for targeted management strategies.

The predictive value of estimated pulse wave velocity (ePWV) combined with BMI for newly diagnosed diabetes

PurposeEstimated pulse wave velocity (ePWV) and body mass index (BMI) are significant predictors of new-onset diabetes. This study aims to evaluate the impact and predictive value of combining ePWV and BMI on the incidence of new-onset diabetes. MethodsA secondary analysis was conducted on a cohort study by Rich Healthcare (China), involving 211,833 eligible participants. Logistic regression analysis identified factors influencing diabetes occurrence, while ROC curve analysis assessed the predictive value of ePWV, BMI, and their combination for new-onset diabetes. ResultsOver a mean follow-up period of 3.12 years, 3,000 men (1.41%) and 1,174 women (0.55%) were diagnosed with diabetes. Logistic regression revealed that BMI, triglycerides, alanine aminotransferase, blood urea nitrogen, creatinine clearance rate, ePWV, and family history of diabetes are high-risk factors for new-onset diabetes. The combination of ePWV and BMI provided a higher area under the ROC curve (0.822) compared to ePWV or BMI alone. ConclusionElevated levels of ePWV and BMI are independent risk factors for new-onset diabetes. Combining these measures enhances predictive accuracy compared to using either indicator alone.

Inflammation, oxidative stress and gut microbiome perturbation: A narrative review of mechanisms and treatment of the alcohol hangover.

Alcohol is the most widely abused substance in the world, the leading source of mortality in 15-49-year-olds, and a major risk factor for heart disease, liver disease, diabetes, and cancer. Despite this, alcohol is regularly misused in wider society. Consumers of excess alcohol often note a constellation of negative symptoms, known as the alcohol hangover. However, the alcohol hangover is not considered to have long-term clinical significance by clinicians or consumers. We undertook a critical review of the literature to demonstrate the pathophysiological mechanisms of the alcohol hangover. Hereafter, the alcohol hangover is re-defined as a manifestation of sickness behavior secondary to alcohol-induced inflammation, using the Bradford-Hill criteria to demonstrate causation above correlation. Alcohol causes inflammation through oxidative stress and endotoxemia. Alcohol metabolism is oxidative and increased intake causes relative tissue hypoxia and increased free radical generation. Tissue damage ensues through lipid peroxidation and the formation of DNA/protein adducts. Byproducts of alcohol metabolism such as acetaldehyde and congeners, sleep deprivation, and the activation of nonspecific inducible CYP2E1 in alcohol-exposed tissues exacerbate free radical generation. Tissue damage and cell death lead to inflammation, but in the intestine loss of epithelial cells leads to intestinal permeability, allowing the translocation of pathogenic bacteria to the systemic circulation (endotoxemia). This leads to a well-characterized cascade of systemic inflammation, additionally activating toll-like receptor 4 to induce sickness behavior. Considering the evidence, it is suggested that hangover frequency and severity may be predictors of the development of later alcohol-related diseases, meriting formal confirmation in prospective studies. In light of the mechanisms of alcohol-mediated inflammation, research into gut permeability and the gut microbiome may be an exciting future therapeutic avenue to prevent alcohol hangover and other alcohol-related diseases.

CT Quantitation and Prediction of the Risk of Type 2 Diabetes Mellitus in Non-Obese Patients with Pancreatic Fatty Infiltration.

To examine the risk of type 2 diabetes mellitus in non-obese patients with pancreatic fatty infiltration through abdominal computed tomography (CT) quantitation. We carried out a retrospective analysis of abdominal CT and inpatient medical records of 238 inpatients from July 2019 to April 2021. The patients were divided into a normal non-obese group (BMI < 25, n = 135) and diabetic non-obese group (BMI < 25, n = 103). Abdominal CT-related parameters included body width; mean CT values of the pancreas, liver, and spleen; difference between pancreas and spleen CT values (P-S); pancreas-to-spleen attenuation ratio (P/S); and liver-to-spleen attenuation ratio (L/S). Logistic regression was used to estimate the risk factors for comorbid diabetes in a non-obese population. The P-values of the pancreas CT value, P-S, P/S, body width, and L/S were all <0.05 and correlated to comorbid diabetes in non-obese patients. Worsening pancreatic fatty infiltration increased the risk of developing diabetes. Using a P/S of 1.0 as reference, every successive decrease in this ratio by 0.1 increases patient risk by 3.981, 4.452, 6.037, and 12.937 times. The risk of developing type 2 diabetes mellitus in non-obese patients increases with the degree of pancreatic fatty infiltration as assessed by CT.

Leveraging Machine Learning and Long-Short Term Memory Algorithm for Early Prediction of Diabetes

Diabetes, a chronic condition, affects numerous populations. Poor insulin production from the pancreas combined with high blood sugar levels can result in the onset of diabetes. Diabetes can be caused by numerous factors. Observe and prevent these factors to reduce the high prevalence of diabetes. This study concentrates on medical record data for determining diabetes risk factors via statistical correlation analysis. These factors will be utilized as machine learning and LSTM input parameters for diabetes prediction. The factors analyzed include blood glucose levels, HbA1c levels, age, BMI, hypertension, heart disease, smoking habits, and gender. Based on the research results, we found that glucose levels (&gt;137 mg/dL) and HbA1c levels (&gt;6.5%) are the main benchmarks in diagnosing diabetes. It is also supported by the correlation value, which is relatively high (0.42 and 0.40, respectively) compared to other factors. Increasing age and BMI also increase the risk of developing diabetes. Comorbidities (such as hypertension or heart disease) and smoking habits can worsen the condition of people with diabetes. Meanwhile (based on gender), women are more at risk of developing diabetes than men because their body mass index increases during the monthly cycle. Apart from that, there is a tendency for blood sugar levels in women to increase in the last two weeks before menstruation. Based on the prediction results, the highest levels of accuracy, sensitivity, and F1 score were obtained (96.97%, 99.97%, and 98.37%) using the LSTM method. This performance shows that LSTM is relatively good for the diabetes prediction process based on existing factors/parameters.