Undiagnosed Diabetic Patients Research Articles

Evaluation of Diabetes Risk Score Tool for Detecting Undiagnosed Type 2 Diabetes Mellitus in Attendees of Referral Clinics at Primary Health Care Centers, Sudan

Introduction: The prevalence of diabetes in Sudan is increasing; however, suitable risk assessment and screening tools to identify at-risk individuals are lacking. Objective: To evaluate the Diabetes Risk Score (DRS) tool for detecting Type 2 Diabetes Mellitus among undiagnosed individuals. Methods: In this cross-sectional study, 214 individuals were recruited from Primary Health Care Referral Centers in Khartoum State. Attendees were interviewed to fill out the Diabetes Risk Score (DRS) questionnaire. Random blood glucose and glycosylated hemoglobin (HbA1c) were tested. Descriptive statistics and sensitivity analysis were performed to test the applicability of the DRS in Sudan. Results: The prevalence of undiagnosed diabetes was found to be 14%. Regarding blood tests, Random blood glucose (RBG) was normal in 93% of the participants (cutoff ≤140 mg/dl). The HbA1c result was normal in 86% of the participants (cutoff < 6.4%). The DRS was high in 40.2% (cutoff ≥33), while 59.8% were considered moderate to low risk. The DRS had a sensitivity of 83.33% and a specificity of 66.85%. The positive and negative predictive values were 29.07% and 96.09%, respectively. The Area Under the Curve (AUC) for detecting undiagnosed diabetes was 0.751 (95% CI: 0.662-0.840). Conclusion: The DRS tool was found to be applicable with reference to the HbA1c test for predicting undiagnosed diabetic patients.

A Comparative Analysis of Machine Learning Models for the Detection of Undiagnosed Diabetes Patients

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

Evaluation of Diabetes Risk Score Tool for Detecting Undiagnosed Type 2 Diabetes Mellitus in Attendees of Referral Clinics at Primary Health Care Centers, Sudan

Introduction: The prevalence of diabetes in Sudan is increasing; however, suitable risk assessment and screening tools to identify at-risk individuals are lacking. Objective: To evaluate the Diabetes Risk Score (DRS) tool for detecting Type 2 Diabetes Mellitus among undiagnosed individuals. Methods: In this cross-sectional study, 214 individuals were recruited from Primary Health Care Referral Centers in Khartoum State. Attendees were interviewed to fill out the Diabetes Risk Score (DRS) questionnaire. Random blood glucose and glycosylated hemoglobin (HbA1c) were tested. Descriptive statistics and sensitivity analysis were performed to test the applicability of the DRS in Sudan. Results: The prevalence of undiagnosed diabetes was found to be 14%. Regarding blood tests, Random blood glucose (RBG) was normal in 93% of the participants (cutoff ≤140 mg/dl). The HbA1c result was normal in 86% of the participants (cutoff < 6.4%). The DRS was high in 40.2% (cutoff ≥33), while 59.8% were considered moderate to low risk. The DRS had a sensitivity of 83.33% and a specificity of 66.85%. The positive and negative predictive values were 29.07% and 96.09%, respectively. The Area Under the Curve (AUC) for detecting undiagnosed diabetes was 0.95 with a confidence level (CI 0.92 to 0.98). Conclusion: The DRS tool was found to be applicable with reference to the HbA1c test for predicting undiagnosed diabetic patients.

Characterization of Patients with Type 2 Diabetes Mellitus and covid-19 in Primary Care

Objectives: To describe the characteristics of the population diagnosed with type 2 Diabetes Mellitus (dm2) infected by sars-CoV-2, and to evaluate whether there is an association between dm2 history and covid-19 severity. Methods: non-probabilistic by convenience sampling, information was obtained from the Online Notification System for Epidemiological Surveillance (sinolave) of the Family Medicine Unit No. 28 of the Mexican Institute of Social Security. A total of 1688 confirmed cases of covid-19 were identified and grouped into patients with and without dm2. Bivariate statistical analysis was performed with Excel 2019 and Stata v. 15.1 programs; measures of association were used using Poisson logistic regression and χ2 test with statistical significance <0.05. Results: it was observed that, in patients with covid-19 and dm2, the prevalence ratio of severe acute respiratory infection, diagnosis of pneumonia, hospitalization, and death were higher compared to the group without dm2. Conclusion: the frequency, of unfavorable characteristics, was higher in the group of patients with dm2. Health conditions caused by covid-19 reinforce the relevance of an intentional search for undiagnosed diabetic patients, untreated or under treatment with poor glycemic control, in order to avoid major health complications.

The Importance of Prediabetes Screening in the Prevention of Cardiovascular Disease.

Prediabetes is a disordered state of glucose metabolism defined by an elevated blood glucose level that is below the level required for the diagnosis of diabetes. Prediabetes is associated with an increased risk of cardiovascular disease. The onset and progression of macrovascular disease occur during the prediabetes phase. Early diagnosis and screening of prediabetes are essential steps to prevent diabetes and its associated complications. To assess the prevalence of prediabetes and undiagnosed diabetes in patients with cardiovascular disease according to the ADA criteria. This cross-sectional study included 2968 a high cardiovascular risk patients aged 40 to 75 years admitted to the Department of Internal Medicine. Sociodemographic variables and other relevant medical history information were collected by the researchers during the clinical interview. A fasting blood sample was obtained to determine HbA1c levels and other relevant laboratory findings. Of the total number of participants, 1496 participants were not diagnosed with diabetes, 485 (32.4%) of them had HbA1c values indicating prediabetes and 158 (10.6%) of them had HbA1c values indicating new diagnosed diabetes. Up to one-third of those with undiagnosed prediabetes had already been diagnosed with cardiovascular complications. Routine screening of glycemic metabolism could be valuable in identifying high-risk individuals before a cardiovascular event occurs.

65-OR: A Novel Electronic Medical Record–Based Diabetes Screening Program in the Emergency Department of an Urban Academic Medical Institution

Despite decades of research on strategies to address disparities in diabetes screening, prevention and treatment, disparities particularly by race/ethnicity and socioeconomic status persist. An innovative approach to diabetes screening was therefore piloted in the Emergency Department (ED) at the University of Illinois Hospital in Chicago from February to April 2021. A best practices advisory (BPA) was built into the electronic medical records system that flagged ED patients at risk for diabetes for a provider to add a A1c test to an already planned blood specimen. The criteria were: all patients age >= 45 years, or patients age 18-44 years with a BMI >= 25; and, with no history of diabetes in the chart and no A1c performed in the last 3 years. Of the 8441 ED visits during the pilot timeframe, the BPA was triggered in 2691, and 2118 visits had a A1c resulted. Approximately half of the results (52%, n=1098) were abnormal. Of the abnormal results, 70% were in the prediabetes range (A1c 5.7-6.4%) and 30% in the diabetes range (A1c >= 6.5%; with 62 of the A1c results >=10%) . As A1c results were not immediately available in the ED, study staff successfully contacted 352 patients with an abnormal result by phone; the remaining were sent a letter informing them of their abnormal result. Of the 352, the average age was 52.2 years, 55% were female, 65% Non-Hispanic Black, 20% Hispanic, 15% Non-Hispanic White/Other. Half were on either Medicare or Medicaid, and 4% uninsured. The median income of the patients’ zipcodes fell at the 44th percentile of the U.S. income. During this initial phase, our novel screening in the ED identified a significant number of patients with undiagnosed diabetes, particularly minority and poor patients, indicating this approach could be leveraged to further stymie health disparities. Future research will now test the linkage of these new patients to diabetes education and care within the health care system. Disclosure K.K.Danielson: None. B.Rydzon: None. Y.Eisenberg: None. B.T.Layden: None. J.Lin: Research Support; Gilead Sciences, Inc., Novo Nordisk. Funding Novo Nordisk (G2111)

Prevalence of Diabetic Retinopathy in Undiagnosed Diabetic Patients: A Nationwide Population-Based Study.

BackgroundWe investigated the prevalence of diabetic retinopathy (DR) in patients with undiagnosed diabetes through a nationwide survey, compared to those with known diabetes.MethodsAmong the participants of the Korean National Health and Nutrition Examination Surveys (KNHANES) from 2017 to 2018, individuals aged ≥40 years with diabetes and fundus exam results were enrolled. Sampling weights were applied to represent the entire Korean population. Newly detected diabetes patients through KNHANES were classified under “undiagnosed diabetes.”ResultsAmong a total of 9,108 participants aged ≥40 years, 951 were selected for analysis. Of them, 31.3% (standard error, ±2.0%) were classified under “undiagnosed diabetes.” The prevalence of DR in patients with known and undiagnosed diabetes was 24.5%±2.0% and 10.7%±2.2%, respectively (P<0.001). The DR prevalence increased with rising glycosylated hemoglobin (HbA1c) levels in patients with known and undiagnosed diabetes (P for trend=0.001 in both). Among those with undiagnosed diabetes, the prevalence of DR was 6.9%±2.1%, 8.0%±3.4%, 5.6%±5.7%, 16.7%±9.4%, and 42.6%±14.8% for HbA1c levels of <7.0%, 7.0%–7.9%, 8.0%–8.9%, 9.0%–9.9%, and ≥10.0% respectively. There was no difference in the prevalence of hypertension, dyslipidemia, hypertriglyceridemia, or obesity according to the presence or absence of DR.ConclusionAbout one-third of patients with diabetes were unaware of their diabetes, and 10% of them have already developed DR. Considering increasing the prevalence of DR according to HbA1c level was found in patients with undiagnosed diabetes like those with known diabetes, screening and early detection of diabetes and DR are important.

Type 2 diabetes mellitus and oral health problems Cross-sectional study

In Egypt, diabetes mellitus is considered to be a foremost health issue with a major influence on the disease and the rate of death. Diabetes mellitus is a communal disorder having affiliated oral disorders that influence dental care. The purpose of this study was to study the prevalence of oral changes in diabetes type 2 in the Egyptian participants to raise awareness between diabetic patients and dental professionals. Methods: This was a cross-sectional study that was done on 200 patients who were diagnosed with diabetes mellitus type 2 at Diabetes and Endocrinology Clinic in Kasr Al Ainy, Cairo University Hospital over a period of 2 months from the first of September 2019 to the first of November 2019. Both genders were selected with age ranging from (32-60) . A detailed oral examination was performed in accordance with international criteria. Results: The most prevalent oral changes were caries (58%), periodontitis (56%), xerostomia (45%), taste dysfunction (32.5%), burning sensation (20%) and candida (15%). Conclusions: This study exhibited that diabetes has harmful influences on oral health status; hence, dentists must be acquainted with the related oral disorders in patients with diabetes as chronic oral complications in these patients could worsen their glucose level and their prognosis. The dentists could have a helpful role in spotting the undiagnosed diabetic patients from the associated oral manifestations.

Digital development for a progressive networked society

Digital development for a progressive networked society

Burden of Undiagnosed Type 2 Diabetes in Diabetic Kidney Disease: A Japanese Retrospective Cohort Study.

The risk of developing diabetic kidney disease (DKD) in patients with undiagnosed diabetes mellitus (UD) has never been evaluated. We studied the burden of UD on the risk of developing DKD in the Japanese population in a single-center retrospective cohort study. The patients with type 2 diabetes mellitus, but without DKD (estimated glomerular filtration rate (eGFR) < 60 mL/min/1.73 m2 or proteinuria), were recruited from January 2018 to January 2019; medical records were scrutinized retrospectively from January 2003 until May 2019. The individuals, with diabetes that could not be denied based on past and current records, comprised the undiagnosed diabetes (UD) group whereas those with confirmed diagnosis comprised the diagnosed diabetes (DD) group. The group differences were tested using a Kaplan–Meier curve and Cox proportional hazards model. Among the 408 participants, 164 (40.2%) and 244 (59.8%) comprised the DD and UD groups, respectively. The baseline parameters, including age, male gender, and BMI were comparable between the groups, but the plasma glucose, HbA1c levels, and diabetic retinopathy prevalence were higher in the UD group. The risk of developing DKD (log rank test, p < 0.001), an eGFR of < 60 mL/min/1.73 m2 (p = 0.001) and proteinuria (p = 0.007) were also higher in the UD group. The unadjusted and adjusted hazard ratios for DKD were 1.760 ((95% CI: 1.323–2.341), p < 0.001) and 1.566 ((95% CI: 1.159–2.115), p = 0.003), respectively, for the UD group. In conclusion, this is the first report showing that UD is a strong risk factor for DKD. The notion that a longer duration of untreated diabetes mellitus is involved strongly in the risk of developing DKD warrants the need for the identification and monitoring of UD patients.

Chorea hyperglycemia basal ganglia (C-H-BG) syndrome as the first presentation of type 2 diabetes mellitus in a 65-year-old male

Background Chorea Hyperglycemia Basal Ganglia (C-H-BG) Syndrome is a known complication of nonketotic hyperglycemia among patients with long term uncontrolled diabetes mellitus. The condition is mainly reported in the elderly Asian population with a female preponderance. C-H-BG syndrome in previously undiagnosed diabetic patients also rarely been reported. We report a case of C-H-BG syndrome as the first presentation of Diabetes mellitus in a male patient. To our knowledge this is the first such case reported in Sri Lanka. Case A 65-year-old previously healthy male who presented with acute onset left sided choriform movements for 6 days, had high blood sugar values on admission. He had no previous manifestations of hyperglycemia and no other micro or macro vascular complications of diabetes were noted. His non-contrast CT of the brain (fig.1) revealed characteristic asymmetrical high intensity signals in bilateral basal ganglia. MRI brain revealed non-contrast enhancing T1 high intensity signals in bilateral basal ganglia mainly involving right lentiform nucleus (fig.2) and low intensity in T2 with normal FLAIR images. He found asymptomatic after 2 weeks, with good glycemic control. Conclusion C-H-BG Syndrome should be considered in patients with acute onset hemichorea, irrespective of their previous history of Diabetes mellitus. Good glycemic control alone can cure C-H-BG syndrome in majority of patients.

Detecting Undiagnosed Diabetes: Proof-of-Concept Based on the Health-Information Exchange System of the Veneto Region (North-East Italy).

Diabetes is a chronic illness characterised by elevated blood glucose levels, driving excess mortality. Its prompt detection and accurate management are critical for delaying complications. Nevertheless, diabetes can remain undiagnosed for years from the onset. The identification of undiagnosed diabetes is a public health priority: in Italy, it is estimated that up to 30% of diabetes cases remain undetected, i.e., that ~1.8 million citizens may be unaware they need medical help. Sometimes, this happens even though these subjects undergo routine or emergency check-ups. Veneto, a region in North-East Italy with 4.9 million residents, implements a regional Health Information Exchange system (rHIE) to collect healthcare data, including laboratory reports, and integrate them with administrative claims. Their combination may be instrumental in finding otherwise undetected cases of diabetes. On the one hand, known diabetic patients should have disease management-generated claims; on the other, laboratory test results can be independently evaluated against diagnostic criteria. In the present work, we examined the anonymised claims and laboratory data, extracted from the rHIE, of 23,376 citizens of the Veneto region. We compared their exemptions, diabetes-related hospitalisation discharge codes, and antidiabetic drugs between 2012 and 2018 to the results of their fasting glucose, glycated haemoglobin, and oral glucose tolerance tests in 2017-2018. We identified 1,407 (6.02%) subjects who, according to administrative claims, appear to be free from diabetes, but met at least one laboratory diagnostic criterion. Such a discrepancy suggests that these people may be undiagnosed diabetic patients. To the best of our knowledge, this is the first proof of concept of an automatic system for the detection of undiagnosed diabetes in Italy. Its full integration in the rHIE and its consequent capillary application could potentially reveal thousands of hidden cases throughout Veneto.

Can internet search engine queries be used to diagnose diabetes? Analysis of archival search data.

Diabetes is often diagnosed late. This study aimed to assess the possibility for earlier detection of diabetes from search data, using predictive models trained on large-scale data. We extracted all English-language queries made by people in the USA to Bing during 1year and identified queries containing symptoms of diabetes. We compared the ability of four different prediction models (linear regression, logistic regression, decision tree and random forest) to distinguish between users who stated that they were diagnosed with diabetes and users who did not refer to diabetes or diabetes drugs but queried about at least one of the symptoms. We identified 11,050 "new diabetes users" who stated they had been diagnosed with diabetes and approximately 11.5 million "control users" who queried about symptoms without querying for terms related to diabetes. Both the logistic regression and the random forest models were able to distinguish between the populations with an area under curve of 0.92 which translates to a positive predictive value of 56% at a false-positive rate of 1%. The model could identify patients up to 240days before they mentioned being diagnosed. Some undiagnosed diabetes patients can be detected accurately according to their symptom queries to a search engine. Such earlier diagnosis, especially in cases of type 1 diabetes, could be clinically meaningful. The ability of search engines to serve as a population-wide screening tool could potentially be improved using additional data provided by users.

Diabetic Ketoacidosis: Review

Diabetic ketoacidosis is defined as a severe metabolic alteration, characterized by the triad of persistent hyperglycemia, metabolic acidosis and hyperketonemia associated with ketonuria. It often occurs in diabetes patientes who have insufficient insulin, routinely undiagnosed diabetic patients or those who have omission in insulin therapy. The diagnosis and treatment of ketoacidosis could be challenging. In view of the importance and increase of cases of ketoacidoses diabetes in dogs and cats, the aims of this review is to assist the veterinarian and veterinary medicine students to understand the pathophysiology, diagnosis and treatment.

Undiagnosed Diabetes in Patients Admitted to a Clinical Decision Unit from the Emergency Department: A Retrospective Review.

ObjectivesDiabetes is a debilitating disease that affects the way the body uses or produces insulin. Research evaluating the usefulness in screening patients admitted to a clinical decision unit (CDU) from the emergency department (ED) has been limited.MethodsA retrospective chart review of patients admitted to a CDU from the ED was performed. Patients included were > 18-year-old who were observed in the CDU, had blood glucose drawn greater than eight hours after admission, and who had not been previously diagnosed with diabetes. Age, sex, and fasting glucose level were collected. The analysis was done to evaluate the percentage of patients undiagnosed and at risk for diabetes mellitus by assessing fasting blood glucose the morning after admission.ResultsStudy revealed that 27.8% of the patients analyzed in this study had fasting blood glucose levels meeting or exceeding the diagnostic threshold of 126 mg/dL and could potentially have undiagnosed diabetes.ConclusionScreening patients admitted to a CDU from the emergency department identified that 27.8% had fasting plasma glucose levels ≥ 126 mg/dL. Consideration should be made to obtain a fasting blood glucose level in those without a previous diagnosis of diabetes who are observed overnight in a CDU.

Glucagon secretion determined by the RIA method is lower in patients with low left ventricular ejection fraction: The new glass study

AimsWe investigated the glucagon levels in patients with heart failure (HF), using long oral glucose tolerance test (OGTT). MethodsIn this prospective observational study, we enrolled 30 undiagnosed diabetes patients (age 69 ± 10 years, 70% males, HbA1c 43 mmol/mol). A 4-h OGTT was performed. Glucose, insulin, and glucagon (radioimmunoassay [RIA] and sandwich ELISA [S-W] methods) were evaluated during 4-h. We compared glucagon levels between HF and non-HF patients. ResultsThere were 11 HF and 19 non-HF patients. In patients with HF, glucagon (S-W) during 4-h was lower than in patients without HF, with no significant difference. The area under the curve (AUC) of glucagon (RIA) during 4-h was significantly lower among HF patients. Moreover, in patients with reduced left ventricular ejection fraction (LVEF) (<40%), AUC glucagon (RIA) was significantly lower than in patients with non-reduced EF (≥40%). However, there was no difference in glucagon values between the high E/e′ (≥13.0) and low E/e′ (<13.0) groups. ConclusionsAlthough glucagon (S-W) showed no significant difference in patients with and without HF, especially reduced LVEF, glucagon (RIA) secretion was significantly lower in HF patients than in patients without HF. It is suggested that low glucagon secretion might be correlated with low EF.

Study on the Screening and Influence Factors for Prediabetes or Diabetes in Primary Health Care Centers of Shanghai

Background: Many studies have reported that diabetes mellitus show the low age tendency. Undiagnosed diabetes patients account for about 25-30% of diabetic patients. Objective: This study aimed to analyze the epidemiological status and influence factors of prediabetes and diabetes, in order to build screening and intervention system for high-risk population in the primary health care centers. Methods: A cross-sectional study was conducted. Residents over 35 years old were randomly enrolled from Jun 2016 to Aug 2017 in Electric Health Record (EHR) of Shanghai Minhang District. Data were collected by using questionnaire and physical examination and blood glucose test. Logistic regression was applied to analyze the risk factors for prediabetes and diabetic patients. Results: A total of 12136 subjects were participated in this screening. Results indicated that 14.54% (n=1765) were diagnosed with diabetes and the prevalence rate in males (17.32%) was significantly higher than in females (12.97%) in each age groups (P&amp;lt; 0.001). Furthermore, 20.69% (n=2511) were diagnosed with prediabetes and males were (23.40%) higher than females (19.16%) (P&amp;lt;0.001). Obesity (BMI≥28)(OR=2.35), family history (OR=1.71), history of cardiovascular disease (OR =1.42), hypertension (OR=1.17), central obesity (OR=1.22), age (OR=1.03), gender (OR=1.31) and sedentary (OR=1.02) were the risk factor for prediabetes or diabetes (p&amp;lt;0.001). In addition, we also found that 54.29% (n= 6589) had over 3 risk factors. Conclusions: It is concluded that we should pay more attention to population with family history and obesity in the screening of diabetes. Grading management and screening should be carried out in different stages of diabetes. Comprehensive prevention and control should be taken to slow progress in diabetes. Disclosure C. Yingling: None.

Comparing biophysical parameters and risk factors amongst known diabetic and undiagnosed diabetic patients

Introduction: diabetes mellitus stands today as a major public health challenge and the prevalence of type 2 diabetes are increasing worldwide. Sub-Saharan Africa also presents a high prevalence of undiagnosed diabetes mellitus which may remain for many years leading to long-term organ damage. Risk factors for diabetes are known and their reduction in a given population has been shown to curb the diabetes epidemic. The main objective was to measure and compare the differences in biophysical parameters in a group of self-selected patients with diabetes and newly-screened diabetic patients.

Prevalence of undiagnosed diabetes mellitus in acute coronary syndrome patients: A hospital-based study

Introduction: Diabetes mellitus and cardiovascular disease are two widely connected entities. The prevalence of undiagnosed diabetes in and impact on survival of acute coronary syndrome (ACS) patients is largely unknown in our population. Aims and Objectives: To study the prevalence of undiagnosed diabetes mellitus in ACS patients and study short-term all-cause mortality in different diabetic and non-diabetic subgroups a hospital-based study. Materials and Methods: The study was conducted in the Post-graduate Department of Medicine at Government Medical College, Srinagar, Jammu and Kashmir, India. It was prospective observational study. It included all patients, admitted as ACS in Medicine Department in between the period from April 2014 to August 2015 with a diagnosis of acute coronary syndromes which include ST-elevation myocardial infarction (STEMI), non-STEMI, and unstable angina. Patients were categorized into five groups known Type 2 diabetes mellitus, prediabetes, first time detected diabetes, non-diabetic ACS patients, and stress-induced hyperglycemia. All-cause 30 days mortality was seen in all patient taken under study. Results: In our study, total of 693 ACS patients were enrolled. The mean age of patients was 61.20 ± 11.69 years. Males predominated in comparison to females with 4:1 ratio. 102 (14.7%) had undiagnosed (first time detected) diabetes. Diabetic and 1st time detected diabetic had double the mortality as compared to the normal and pre-diabetic group (10.8%, 8.8% vs. 5.8%). Conclusion: Undiagnosed diabetes in patients with ACS is not uncommon in our population. Diabetic status profoundly affects the short-term mortality of ACS patients.

Prevalence of Prediabetes and Undiagnosed Diabetes in Patients with HFpEF and HFrEF and Associated Clinical Outcomes

PurposeThe prevalence and consequences of prediabetic dysglycemia and undiagnosed diabetes is unknown in patients with heart failure (HF) and preserved ejection fraction (HFpEF) and has not been compared to heart failure and reduced ejection fraction (HFrEF).MethodsWe examined the prevalence and outcomes associated with normoglycemia, prediabetic dysglycemia and diabetes (diagnosed and undiagnosed) among individuals with a baseline glycated hemoglobin (hemoglobin A1c, HbA1c) measurement stratified by HFrEF or HFpEF in the Candesartan in Heart failure Assessment of Reduction in Mortality and morbidity programme (CHARM). We studied the primary outcome of HF hospitalization or cardiovascular (CV) death, and all-cause death, and estimated hazard ratios (HR) by use of multivariable Cox regression models.ResultsHbA1c was measured at baseline in CHARM patients enrolled in the USA and Canada and was available in 1072/3023 (35%) of patients with HFpEF and 1578/4576 (34%) patients with HFrEF. 18 and 16% had normoglycemia (HbA1c < 6.0), 20 and 22% had prediabetes (HbA1c 6.0–6.4), respectively. Finally among patients with HFpEF 22% had undiagnosed diabetes (HbA1c > 6.4), and 40% had known diabetes (any HbA1c), with corresponding prevalence among HFrEF patients being 26 and 35%. The rates of both clinical outcomes of interest were higher in patients with undiagnosed diabetes and prediabetes, compared to normoglycemic patients, irrespective of HF subtype, and in general higher among HFrEF patients. For the primary composite outcome among HFpEF patients, the HRs were 1.02 (95% CI 0.63–1.65) for prediabetes, HR 1.18 (0.75–1.86) for undiagnosed diabetes and 2.75 (1.83–4.11) for known diabetes, respectively, p value for trend across groups < 0.001. Dysglycemia was also associated with worse outcomes in HFrEF.ConclusionsThese findings confirm the remarkably high prevalence of dysglycemia in heart failure irrespective of ejection fraction phenotype, and demonstrate that dysglycemia is associated with a higher risk of adverse clinical outcomes, even before the diagnosis of diabetes and institution of glucose lowering therapy in patients with HFpEF as well as HFrEF.