Number Of Diabetes Cases Research Articles

Drug Related Problems (DRPs) penggunaan obat antidiabetik pada pasien rawat inap diabetes mellitus tipe 2

rates continue to rise annually. Indonesia ranks fifth globally in the number of diabetes cases. Drug-related problems (DRPs) are common in clinical practice and can occur at all stages of the treatment process. Objective: This study aims to identify DRPs in hospitalized patients with Type 2 Diabetes Mellitus. Methods: This descriptive study employed a cross-sectional approach. Data were collected retrospectively from medical records of 61 hospitalized patients with Type 2 Diabetes Mellitus at a hospital in Jakarta. Results: The analysis of DRPs in hospitalized patients with Type 2 Diabetes Mellitus revealed that 5 cases (14%) involved inappropriate drug selection, 6 cases (17%) involved suboptimal dosing, 10 cases (29%) involved adverse drug reactions (ADRs), and 14 cases (40%) involved drug interactions. Conclusion: Polypharmacy requires careful attention as it has the potential to cause drug interactions and adverse drug reactions.

Parkia javanica Edible Pods Reveal Potential as an Anti-Diabetic Agent: UHPLC-QTOF-MS/MS-Based Chemical Profiling, In Silico, In Vitro, In Vivo, and Oxidative Stress Studies.

According to the World Health Organization, over 422 million people worldwide have diabetes, with the majority residing in low- and middle-income countries. Diabetes causes 1.5 million fatalities a year. The number of diabetes cases and its prevalence have progressively increased over the last few decades. This study aims to determine the phytochemicals in the edible part of Perkia javanica, predict their α-glucosidase inhibitory potential, one of the promising targets for diabetes, and then carry out in vitro and in vivo studies. The phytochemicals present in the n-butanol fraction of the methanol extract of P. javanica pods were analyzed using UHPLC-QTOF-MS/MS (Ultra-High-Performance Liquid Chromatography-Quadrupole Time-of-Flight Mass Spectrometry). The UHPLC-QTOF analysis revealed the presence of 79 different compounds in the n-butanol fraction. Among these, six compounds demonstrated excellent binding affinities with α-glucosidase, surpassing the performance of two standard inhibitors, Miglitol and Voglibose. In vitro α-glucosidase inhibitory activities were assessed by the n-butanol fraction, followed by in vivo studies. According to the in vitro study, the inhibitory efficiency against α-glucosidase was determined to have an IC50 value of 261.9 µg/mL. The in vivo findings revealed a significant reduction in blood glucose levels in Swiss albino mice treated with the same extract, decreasing from 462.66 mg/dL to 228.66 mg/dL. Additionally, the extract significantly increased the activity of the enzymes catalase and superoxide dismutase (SOD) and decreased the amount of malondialdehyde (MDA) in the liver and kidney tissue. The predicted physicochemical parameters indicated that most of the compounds would be excreted from the body after inhibition in the small intestine without being absorbed. Considering the low cost and wide availability of raw materials, P. javanica pods can serve as a good food supplement that may help prevent type 2 diabetes management.

Intelligent optimal control of nonlinear diabetic population dynamics system using a genetic algorithm

Diabetes is a chronic disease affecting millions of people worldwide. Several studies have been carried out to control the diabetes problem, involving both linear and non-linear models. However, the complexity of linear models makes it impossible to describe the diabetic population dynamic in depth. To capture more detail about this dynamic, non-linear terms were introduced into the mathematical models, resulting in more complicated models strongly consistent with reality (capable of re-producing observable data). The most commonly used methods for control estimation are Pantryagain’s maximum principle and Gumel’s numerical method. However, these methods lead to a costly strategy regarding material and human resources; in addition, diabetologists cannot use the formulas implemented by the proposed controls. In this paper, the authors propose a straightforward and well-performing strategy based on non-linear models and genetic algorithms (GA) that consists of three steps: 1) discretization of the considered non-linear model using classical numerical methods (trapezoidal rule and Euler–Cauchy algorithm); 2) estimation of the optimal control, in several points, based on GA with appropriate fitness function and suitable genetic operators (mutation, crossover, and selection); 3) construction of the optimal control using an interpolation model (splines). The results show that the use of the GA for non-linear models was successfully solved, resulting in a control approach that shows a significant decrease in the number of diabetes cases and diabetics with complications. Remarkably, this result is achieved using less than 70% of available resources.

An investigation of machine learning algorithms and data augmentation techniques for diabetes diagnosis using class imbalanced BRFSS dataset

Diabetes is a prevalent chronic condition that poses significant challenges to early diagnosis and identifying at-risk individuals. Machine learning plays a crucial role in diabetes detection by leveraging its ability to process large volumes of data and identify complex patterns. However, imbalanced data, where the number of diabetic cases is substantially smaller than non-diabetic cases, complicates the identification of individuals with diabetes using machine learning algorithms. This study focuses on predicting whether a person is at risk of diabetes, considering the individual’s health and socio-economic conditions while mitigating the challenges posed by imbalanced data. We employ several data augmentation techniques, such as oversampling (Synthetic Minority Over Sampling for Nominal Data, i.e.SMOTE-N), undersampling (Edited Nearest Neighbor, i.e. ENN), and hybrid sampling techniques (SMOTE-Tomek and SMOTE-ENN) on training data before applying machine learning algorithms to minimize the impact of imbalanced data. Our study sheds light on the significance of carefully utilizing data augmentation techniques without any data leakage to enhance the effectiveness of machine learning algorithms. Moreover, it offers a complete machine learning structure for healthcare practitioners, from data obtaining to machine learning prediction, enabling them to make informed decisions.

RISK ASSESSMENT OF DIABETES USING INDIAN DIABETES RISK SCORE IN NON DIABETIC INDIVIDUALS

Background and Objectives: Diabetes has emerged as a major health challenge in India due to a rapid rise in the number of diabetes cases. Early identification of high risk individuals through screening and early interventions in the form of lifestyle modifications and treatment would help in the prevention of diabetes and its complications. This study was done to assess the risk of type 2 diabetes mellitus (T2DM) in an urban slum population using the Indian Diabetes Risk Score (IDRS) and to determine the factors associated with high risk score. Methods: A cross-sectional study was conducted among Department of Gen. Medicine Sri Aurobindo Medical College and Post Graduate Institute, Indore (18 Months). With the institutional ethical committees permission. A total of 100 study participants were selected randomly. A pre-designed and pre-tested structured questionnaire was used for data collection. Assessment of risk of T2DM was done using the IDRS. Results: Of the 100 study participants, 74% were at high risk (IDRS ≥60) followed by 24% at moderate risk (IDRS 30-50) and 2% at low risk (IDRS <30). 45 individuals in the age group ≥50 yr were at high risk compared to 25 in 35-49 yr age group. Most (n=25, 87.5%) of sedentary workers were at high risk compared to those employed in moderate (n=38, 75.4%) and strenuous work (n=10, 51.9%). Interpretation & Conclusions: 74.3 percent of study participants were high-risk for T2DM. High-risk IDRS scores were associated with age, occupation, abdominal obesity, general obesity, and high blood pressure.

Diabetes, Part 1: Disease State Review.

The art and science of diabetes care, education, and management has evolved significantly over the last few decades. Yet, the United States is experiencing an escalation in the number of diabetes cases. Diabetes is a highly prevalent health condition in the aging population with more than 9 million (26.8%) people 65 years of age and older having diabetes and over 50% with pre-diabetes. In older patients, it is imperative to know the type of diabetes a person has is accurately diagnosed to ensure the correct treatment is used. Pharmacists need to individualize treatment goals and develop optimal therapy plans that focus beyond glycemic management and consider cardiovascular (CV), renal, weight, and hypoglycemic risks and benefits for effective and efficacious care in older patients.

Associations of imbalance of intestinal flora with severity of disease, inflammatory factors, adiponectin, and vascular endothelial function of hypertension patients.

To explore the relationship between the severity of hypertension and the imbalanced intestinal flora, inflammatory factors, adiponectin (ADPN) and vascular endothelial function in primary hypertension patients. According to the grading criteria for hypertension, in total of 60 patients with primary hypertension in our hospital from April to July, 2020 were divided into Grade 1 group (n = 20), Grade 2 group (n = 20), and Grade 3 group (n = 20). The feces of the research subjects were collected to extract the deoxyribonucleic acid (DNA) and detect its composition of intestinal flora. Subsequently, the peripheral blood was collected to determine the changes in inflammatory factors interleukin-2 (IL-2), IL-4, tumor necrosis factor-α (TNF-α) and IL-1β, serum immunoglobulin G (IgG) and IgM, ADPN and vascular endothelial function-related endothelin-1 (ET-1), nitric oxide (NO), vascular endothelial growth factor (VEGF), and intercellular adhesion molecule-1 (ICAM-1). There were no significant differences in the gender, age, and body mass index (BMI), the proportion of smokers, diet habit, probiotics and antihypertensive medication use, and number of diabetic cases among groups (p > 0.05). We found an inverse association between blood pressure measures and microbial diversity, in particular microbial richness (p < 0.05). Among the four major kinds of intestinal flora, the composition of firmicutes (p < 0.05) and bacteroidetes (p < 0.05) showed obvious differences among the three groups, and they had consistent trends with the changes in the abundance of firmicutes and bacteroidetes. Intestinal flora imbalance is closely related to the severity of hypertension, inflammatory factors, ADPN, and vascular endothelial function.

SOCIAL AND ECONOMIC ASPECTS OF THE INCREASING INCIDENCE OF DIABETES IN VARNA AND BULGARIA

Objective: The impact of socio-economic factors on the increased incidence of chronic diseases requires changes in the policy of health authorities. In view of that, the study tried to investigate the role and consequences of those factors on T2DM and thus contribute to future development of comprehensive programmes for screening, diagnostics and treatment. Methods and materials: Forecasting the incidence rates of diabetes on a national and regional scale using regression analysis of results from municipal programs between 2015 and 2019 in Varna. The following models were studied: linear, cubic, quadratic, exponential, logarithmic, power, growth and inversion. Data processing and analysis were performed with the software set SPSS Statistics and graphical analysis with Microsoft Excel. Results: A constant rise in the number of diabetes cases was identified. At national level, mortality among females was higher than mortality in males, while at local level (Varna), such correlation was not established. Costs for the studied groups steadily increased during the studied period. Healthcare services for people with diabetes mellitus cost 2.5 times more than that of people without diabetes of the same age group.

Forecasting Diabetes Cases Prevented and Cost Savings Associated with Population Increases of Walking in the Greater Toronto and Hamilton Area, Canada.

Promoting adequate levels of physical activity in the population is important for diabetes prevention. However, the scale needed to achieve tangible population benefits is unclear. We aimed to estimate the public health impact of increases in walking as a means of diabetes prevention and health care cost savings attributable to diabetes. We applied the validated Diabetes Population Risk Tool (DPoRT) to the 2015/16 Canadian Community Health Survey for adults aged 18–64, living in the Greater Toronto and Hamilton area, Ontario, Canada. DPoRT was used to generate three population-level scenarios involving increases in walking among individuals with low physical activity levels, low daily step counts and high dependency on non-active forms of travel, compared to a baseline scenario (no change in walking rates). We estimated number of diabetes cases prevented and health care costs saved in each scenario compared with the baseline. Each of the three scenarios predicted a considerable reduction in diabetes and related health care cost savings. In order of impact, the largest population benefits were predicted from targeting populations with low physical activity levels, low daily step counts, and non active transport use. Population increases of walking by 25 min each week was predicted to prevent up to 10.4 thousand diabetes cases and generate CAD 74.4 million in health care cost savings in 10 years. Diabetes reductions and cost savings were projected to be higher if increases of 150 min of walking per week could be achieved at the population-level (up to 54.3 thousand diabetes cases prevented and CAD 386.9 million in health care cost savings). Policy, programming, and community designs that achieve modest increases in population walking could translate to meaningful reductions in the diabetes burden and cost savings to the health care system.

Cost-effectiveness of a lifestyle intervention in high-risk individuals for diabetes in a low- and middle-income setting: Trial-based analysis of the Kerala Diabetes Prevention Program

BackgroundData on the cost-effectiveness of lifestyle-based diabetes prevention programs are mostly from high-income countries, which cannot be extrapolated to low- and middle-income countries. We performed a trial-based cost-effectiveness analysis of a lifestyle intervention targeted at preventing diabetes in India.MethodsThe Kerala Diabetes Prevention Program was a cluster-randomized controlled trial of 1007 individuals conducted in 60 polling areas (electoral divisions) in Kerala state. Participants (30–60 years) were those with a high diabetes risk score and without diabetes on an oral glucose tolerance test. The intervention group received a 12-month peer-support lifestyle intervention involving 15 group sessions delivered in community settings by trained lay peer leaders. There were also linked community activities to sustain behavior change. The control group received a booklet on lifestyle change. Costs were estimated from the health system and societal perspectives, with 2018 as the reference year. Effectiveness was measured in terms of the number of diabetes cases prevented and quality-adjusted life years (QALYs). Three times India’s gross domestic product per capita (US$6108) was used as the cost-effectiveness threshold. The analyses were conducted with a 2-year time horizon. Costs and effects were discounted at 3% per annum. One-way and multi-way sensitivity analyses were performed.ResultsBaseline characteristics were similar in the two study groups. Over 2 years, the intervention resulted in an incremental health system cost of US$2.0 (intervention group: US$303.6; control group: US$301.6), incremental societal cost of US$6.2 (intervention group: US$367.8; control group: US$361.5), absolute risk reduction of 2.1%, and incremental QALYs of 0.04 per person. From a health system perspective, the cost per diabetes case prevented was US$95.2, and the cost per QALY gained was US$50.0. From a societal perspective, the corresponding figures were US$295.1 and US$155.0. For the number of diabetes cases prevented, the probability for the intervention to be cost-effective was 84.0% and 83.1% from the health system and societal perspectives, respectively. The corresponding figures for QALY gained were 99.1% and 97.8%. The results were robust to discounting and sensitivity analyses.ConclusionsA community-based peer-support lifestyle intervention was cost-effective in individuals at high risk of developing diabetes in India over 2 years.Trial registrationThe trial was registered with Australia and New Zealand Clinical Trials Registry (ACTRN12611000262909). Registered 10 March 2011.

171-OR: Preventing Diabetes: Resource Allocation between Risk-Based and Whole Population–Based Approaches

The national diabetes prevention goal set by CDC is to reduce new type 2 diabetes cases from 1.4 million in 2018 to 1.1 million in 2025 (a 21% reduction). Policymakers often debate the merits of interventions that focus on high-risk individuals vs. whole population-based interventions that benefit a broader spectrum of the population. To resolve this debate, we use a simulation modeling approach to optimize resource allocation among a risk-based intervention (the lifestyle change intervention specified in the National Diabetes Prevention Program [National DPP]) and three whole-population interventions (e.g., sugar sweetened beverage taxes, worksite health promotion, and bike lanes or walking paths). We use the simulation model to estimate the cost and number of diabetes cases prevented by combinations of risk-based and whole population-based interventions. We then plot the prevention frontier as the least-cost combination of interventions for each prevention target. The optimal allocation of resources across interventions can be selected by choosing a prevention target or specifying the available prevention budget. We find that neither the National DPP alone nor three whole population-based interventions combined are likely to meet the 2025 goal for type 2 diabetes prevention (Figure). Instead, a combination of the National DPP and whole population-based interventions will be needed. Disclosure S. Neuwahl: None. T.J. Hoerger: Research Support; Self; Centers for Disease Control and Prevention, Centers for Medicare and Medicaid Services. M. Laxy: None. H. Shao: None. A.L. Albright: None. P. Zhang: None. Funding Centers for Disease Control and Prevention

Comparative Study of Iris and Retinal Images for Early Detection of Diabetic Mellitus

The recent increase in the number of diabetic cases due to genetic reasons or sedentary lifestyle, necessitates urgent need for an effective glucose monitoring system. Certainly, periodic glucose level monitoring in the blood will prevent from entering chronic diabetic condition and a noninvasive monitoring tool leads to a simple and automated diagnosis procedure. In this present work, an iridology-based diagnosis of diabetes has been discussed and is compared with a standard retinal imaging modality. Two subject groups, one group of 30 subjects without diabetes, the other group with 20 subjects of controlled diabetes with less than two years duration and 25 subjects with more than two years of uncontrolled diabetes were evaluated. Iris images are acquired using an iriscope and subsequently compared it with that of retinal spectral domain optical coherence tomography (SDOCT) images of the same subjects. The segmentation of the pancreas region in the iris images and the retinal layers in the SDOCT retinal images are performed automatically to predict Diabetic Mellitus (DM). The selected features of the segmented region are given as input to k-Nearest Neighbor, Support Vector Machine and Random Forest classifiers for discriminating diabetic and non-diabetic normal cases. The results showed that iris images are able to reveal the diabetic condition of the subjects even before their retina could reveal the same. However, elaborate extensive studies have to be carried out in order to validate iridology as a best noninvasive tool for detection of diabetes at the early stage itself.

Role of Comprehensive Diabetes Care in Known Diabetes Patients from Vidarbha Region

Introduction: Number of patients with Diabetes Mellitus (DM) have been doubled in past few decades. India is particularly facing impending massive rise in number of diabetic cases, and therefore India is termed as Diabetes capital of the world by most of the clinicians. Worryingly, prevalence in Vidarbha region in India is rising and its figure as per published literature is >5%. Comprehensive diabetes care (CDC) is form of Ayurvedic therapy which combines panchkarma techniques and herbal drugs. Aims and objectives: The present study was initiated to evaluate the effectiveness of CDC in patients of DM in terms of glycosylated haemoglobin (HbA1c), body mass index (BMI), oral glucose tolerance test (OGTT), body weight, abdominal girth, etc. in Vidarbha region of India. Materials and methods: The present observational study was conducted across various Madhavbaug clinics in Vidarbha region of India. Data of the patients who were diagnosed with DM i.e. HbA1c>6.5% and received CDC therapy of 6 settings over 12 weeks was analysed. Results: In the present study, out of 293 type 2 diabetic patients, 179 were males (61%), while 114 were females (39%), thus male: female ratio was 1.57:1. At the end of 12 weeks of CDC therapy, number of patients with controlled DM (154) was significantly increased as compared to baseline (0), while the HbA1c also reduced from 6.89 at week 12 as compared to 8.45 at baseline, which was statistically significant. Other parameters like BMI, abdominal girth, etc. were significantly improved after 12 weeks of CDC therapy. Conclusion: Given the findings of present study, CDC therapy can be used as efficacious alternative form of therapy in the management of DM.

Risk of type 2 diabetes mellitus among urban slum population using Indian Diabetes Risk Score.

Background and objectives:Diabetes has emerged as a major health challenge in India due to a rapid rise in the number of diabetes cases. Early identification of high risk individuals through screening and early interventions in the form of lifestyle modifications and treatment would help in the prevention of diabetes and its complications. This study was done to assess the risk of type 2 diabetes mellitus (T2DM) in an urban slum population using the Indian Diabetes Risk Score (IDRS) and to determine the factors associated with high risk score.Methods:A community-based cross-sectional study was conducted among the urban slum population in North Telangana, India. A total of 136 study participants were selected randomly from the records. A pre-designed and pre-tested structured questionnaire was used for data collection. Assessment of risk of T2DM was done using the IDRS.Results:Of the 136 study participants, 101 (74.3%) were at high risk (IDRS ≥60) followed by 32 (23.5%) at moderate risk (IDRS 30-50) and three (2.2%) at low risk (IDRS <30). Sixty two (92.5%) individuals in the age group ≥50 yr were at high risk compared to 34 (63%) in 35-49 yr age group. Most (n=35, 87.5%) of sedentary workers were at high risk compared to those employed in moderate (n=52, 75.4%) and strenuous work (n=14, 51.9%).Interpretation & conclusions:Nearly three-fourth (74.3%) of the study participants were at a high risk of developing T2DM. Age, type of occupation, abdominal obesity, general obesity and high blood pressure were the factors significantly associated with high risk IDRS score.

Detection of Diabetic Patterns using Supervised Learning

World Health Organization’s (WHO) report 2018, on diabetes has reported that the number of diabetic cases has increased from one hundred eight million to four hundred twenty-two million from the year 1980. The fact sheet shows that there is a major increase in diabetic cases from 4.7% to 8.5% among adults (18 years of age). Major health hazards caused due to diabetes include kidney function failure, heart disease, blindness, stroke, and lower limb dismembering. This article applies supervised machine learning algorithms on the Pima Indian Diabetic dataset to explore various patterns of risks involved using predictive models. Predictive model construction is based upon supervised machine learning algorithms: Naïve Bayes, Decision Tree, Random Forest, Gradient Boosted Tree, and Tree Ensemble. Further, the analytical patterns about these predictive models have been presented based on various performance parameters which include accuracy, precision, recall, and F-measure.

Is Alpha-Glucosidase Inhibition a Mechanism of the Antidiabetic Action of Garlic (&amp;lt;i&amp;gt;Allium sativum&amp;lt;/i&amp;gt;)?

Objective: There has been a global surge in the number of diabetic cases. Many of the agents used as antidiabetic are either expensive or have side effects. Researchers are now turning their attention to phytotherapy as a viable alternative in the treatment of hyperglycemia. The aim of this study was to examine the inhibition of α-glucosidase as a possible mechanism of antidiabetic action of garlic. Method: The inhibitory effect of different concentrations of garlic was examined for alpha-glucosidase inhibitory activity in a 96-well micro plate. Saccharomyces cerevisiae was used as the source of alpha-glucosidase and the assay was analyzed with a Thermo Scientific® Multiskan Spectropho-meter at an absorbance of 400 nm. Result: The extracts of garlic exhibited a dose-dependent inhibition of alpha-glucosidase in comparison to acarbose. The IC50 of acarbose was 3.19 ± 0.42 mg/ml, for garlic, the IC50 was 16.93 mg/ml. Conclusion: In this study, garlic oil showed some promise as an antidiabetic agent with a mechanism of action similar to acarbose and miglitol that are currently used as antidiabetics. It is hoped that carrying out further research on garlic will elucidate other mechanisms of action.

Socioeconomic Relationship Trends between Diabetes and Postcodes in NSW, Australia

Diabetes prevalence is increasing in Australia. The Ausdiab Study 2001 found diabetes affected 7.4% of adults &amp;gt;25y with 0.7% of Australians diagnosed yearly. The distribution of diabetes and standard of care however is not nationally uniform. Health disparities may be due to social disadvantage, ethnicity, age distribution and services access. We examined the distribution of diabetes and level of control based on postcode in NSW at two time periods, to determine if the relationship between the level of care (defined by HbA1c) and geographical distribution had changed with time. Data was sourced from a pathology company performing the bulk of testing, capturing the majority of subjects with diabetes. Subjects were identified by HbA1c measurement. Two periods were examined, 20and 2013. For each subject who had an HbA1c collected, data was extracted for gender, age, postcode, eGFR, lipids and urine microalbumin. Mann Whitney U tests compared differences in parameter medians between the two periods. Spearman correlations and linear regression assessed relationships between socioeconomic disadvantage scale based on postcode (SEIFA), and diabetes control. Data was available for 119018 subjects in 20and 185954 subjects in 2013. During both years, the number of diabetes cases was higher in areas of greater disadvantage and HbA1c was correlated with higher disadvantage score (p&amp;lt;0.01). Control significantly improved over time (Mdn HbA1c 6.5% vs. 6.3%) and the proportion of patients with poor control (HbA1c &amp;gt;7.0%) also improved (36.5% vs. 31.7%) (p&amp;lt;0.01). SEIFA significantly predicted diabetes control in both 20(F (1,119016) =907.4 p&amp;lt;0.01, r2=0.01) and 2013 (F (1,185952)=1940.3 p&amp;lt;0.01, r2=0.01). Control improved within 9/10 SEIFA categories over time (p&amp;lt;0.05) though the gap between socioeconomic tiers remained. Health inequalities in diabetes control parallel socioeconomic disadvantage levels in NSW. Despite an 8 year interim disparities in HbA1c between socioeconomic tiers have not changed. Disclosure K. Burns: None. T. Hng: None. N. Cheung: None.

Hypertriglyceridaemic-waist phenotype and risk of diabetes in people with impaired fasting glucose in primary care: a cohort study.

We aimed to determine the prospective association between baseline triglyceridaemic-waist phenotypes and diabetic mellitus incidence in individuals with impaired fasting glucose seen in primary care. A cohort of 1101 participants (84.4% of the recruited individuals) with impaired fasting glucose were recruited from three primary care clinics during regular follow-ups to monitor their chronic conditions. Baseline triglyceridaemic-waist phenotypes were divided into four groups: (1) normal waistline and triglyceride level (n = 252); (2) isolated central obesity (n = 518); (3) isolated high triglyceride level (n = 80); and (4) central obesity with high triglyceride level (i.e. hypertriglyceridaemic-waist phenotype) (n = 251). The presence of diabetes at follow-up was determined by fasting plasma glucose (≥ 7.0 mmol/l) and/or 2-h 75-g oral glucose tolerance test (≥ 11.1 mmol/l) and/or HbA1c (47.5 mmol/mol; ≥ 6.5%) according to American Diabetes Association diagnostic criteria. Multivariable Cox proportional hazards regressions were established to assess the impact of different triglyceridaemic-waist phenotypes on time to diabetes onset. After a mean follow-up period of 6.5 months (sd 4.7 months), the number of diabetes cases was significantly higher in the group with hypertriglyceridaemic-waist phenotype (52.2%) compared with the other three phenotype groups (group 1: 28.2%; group 2: 34.6%; group 3: 30.0%). Only the hypertriglyceridaemic-waist phenotype showed an increased risk of developing diabetes (hazard ratio 1.581, 95% CI 1.172-2.134; P = 0.003) compared with the group with normal waistline and triglyceride level after controlling for confounders. The combination of central obesity and hypertriglyceridaemia is associated with > 50% risk of progression to diabetes within 6 months among individuals with impaired fasting glucose seen in primary care.

Changing from glucose to HbA1c for diabetes diagnosis: predictive values of one test and importance of analytical bias and imprecision

In Denmark, the use of HbA1c in the diagnosis of diabetes was adopted from March 2012. We evaluated the change in the number of diabetes cases diagnosed by haemoglobin A1c (HbA1c) versus fasting venous plasma glucose (FPG), and estimated the influence of analytical variation and bias on the HbA1c-based prevalence of diabetes. The study population constituted 4239 individuals not known to have diabetes randomly selected from all inhabitants aged 25-75 years in the former County of Vejle, Denmark. The number of undiagnosed patients with diabetes in the study population using FPG or HbA1c as the diagnostic criterion was estimated. Furthermore, changes in the analytical bias and coefficient of variation (CV) for HbA1c analysis were simulated and the effect on the number of diabetes cases was observed. Changing the diagnostic test from FPG to HbA1c reduced the number of patients with diabetes by approximately 46% based on one measurement. The predictive value of one test of HbA1c was 91% versus only 66% for one test of FPG. Analytical variation had a much greater impact on the number of patients with diabetes than bias. At a bias of 0%, an increase of CVanalytical from 2.7% to 3.7% increased the number of diabetes cases by 90%. In the study population, the percentage of undiagnosed patients with diabetes aged 25-75 years was reduced from 3.6% (95% CI 3.0%-4.2%) based on one FPG measurement (FPG ≥7.0 mmol/L) to only 1.9% (95% CI 1.5%-2.3%) if the diagnosis of diabetes was based on the criterion of HbA1c ≥48 mmol/mol (6.5% DCCT).

Administration of Insulin to Type 1 Diabetes-Current Nursing Practice

The International Diabetes Federation's (IDF) diabetes atlas 2011 shows that 366 million people have diabetes worldwide and by 2030, this will have risen to 552 million. At the same time, 183 million people (50%) with diabetes are undiagnosed1. Current estimates suggest that two thirds of those affected by diabetes live in low and middle income countries (LMIC). By 2025, the number of diabetes cases will increase by 170% in low and middle income countries, compared to a 41% increase in developed countries2. The total child population (0–14 years) was 1.9 billion worldwide. Some 78,000 children under 15 years are estimated to develop type 1 diabetes annually worldwide. About 49,000 children were affected with type 1 diabetes with 24% from European region and 23% from South East Asian region1. Insulin is an important hormone concerned with regulation of carbohydrate, protein and fat metabolism and blood glucose level. Insulin has been identified as one of the top 10 high risk medicines in treating type 1 diabetes3.