Identify Type 2 Diabetes Mellitus Patients Research Articles

Coronary inflammation based on pericoronary adipose tissue attenuation in type 2 diabetic mellitus: effect of diabetes management

BackgroundCoronary inflammation plays crucial role in type 2 diabetes mellitus (T2DM) induced cardiovascular complications. Both glucose-lowering drug interventions (GLDIS) and glycemic control (GC) status potentially correlate coronary inflammation, as indicated by changes in pericoronary adipose tissue (PCAT) attenuation, and thus influence cardiovascular risk. This study evaluated the impact of GLDIS and GC status on PCAT attenuation in T2DM patients.MethodsThis retrospective study collected clinical data and coronary computed tomography angiography (CCTA) images of 1,342 patients, including 547 T2DM patients and 795 non-T2DM patients in two tertiary hospitals. T2DM patients were subgroup based on two criteria: (1) GC status: well: HbA1c < 7%, moderate: 7 ≤ HbA1c ≤ 9%, and poor: HbA1c > 9%; (2) GLDIS and non-GLDIS. PCAT attenuations of the left anterior descending artery (LAD-PCAT), left circumflex artery (LCX-PCAT), and right coronary artery (RCA-PCAT) were measured. Propensity matching (PSM) was used to cross compare PCAT attenuation of non-T2DM and all subgroups of T2DM patients. Linear regressions were conducted to evaluate the impact of GC status and GLDIS on PCAT attenuation in T2DM patients.ResultsSignificant differences were observed in RCA-PCAT and LCX-PCAT between poor GC-T2DM and non-T2DM patients (LCX: − 68.75 ± 7.59 HU vs. – 71.93 ± 7.25 HU, p = 0.008; RCA: − 74.37 ± 8.44 HU vs. − 77.2 ± 7.42 HU, p = 0.026). Higher PCAT attenuation was observed in LAD-PCAT, LCX-PCAT, and RCA-PCAT in non-GLDIS T2DM patients compared with GLDIS T2DM patients (LAD: − 78.11 ± 8.01 HU vs. − 75.04 ± 8.26 HU, p = 0.022; LCX: − 71.10 ± 8.13 HU vs. − 68.31 ± 7.90 HU, p = 0.037; RCA: − 78.17 ± 8.64 HU vs. − 73.35 ± 9.32 HU, p = 0.001). In the linear regression, other than sex and duration of diabetes, both metformin and acarbose were found to be significantly associated with lower LAD-PCAT (metformin: β coefficient = − 2.476, p=0.021; acarbose: β coefficient = − 1.841, p = 0.031).ConclusionInadequate diabetes management, including poor GC and lack of GLDIS, may be associated with increased coronary artery inflammation in T2DM patients, as indicated by PCAT attenuation on CCTA, leading to increased cardiovascular risk. This finding could help healthcare providers identify T2DM patients with increased cardiovascular risk, develop improved cardiovascular management programs, and reduce subsequent cardiovascular related mortality.

Evaluating the Feasibility of Pro-Neurotensin and 25-Hydroxyvitamin D3 as Possible Indicators for Type 2 Diabetes Mellitus and Its Complications.

(1) Background: Type 2 diabetes mellitus (T2DM) and metabolic syndrome are associated with decreased vitamin D. In contrast, high pro-neurotensin (pro-NT) levels are linked with an increased risk of T2DM and cardiovascular disease. We aimed to determine the validity of pro-NT and 25-dihydroxy vitamin D3 levels as predictors for T2DM complications; (2) Methods: One hundred T2DM, and one hundred healthy volunteers participated in this case-control study. Their Pro-NT and 25-hydroxyvitamin D3 levels were evaluated using the ELISA technique; (3) Results: Pro-NT and 25 (OH) vitamin D3 have significant validity and accuracy in T2DM prediction, 84.5%, and 90.5%, respectively (p = 0.001). At a value of <29.5, 25-Hydroxy vitamin D3 showed 88% sensitivity and 93% specificity in predicting T2DM. At a value of >124 Pmol/L, Pro-NT showed 81% sensitivity and 88% specificity in predicting T2DM. At a value of 16.5, 25-Hydroxy vitamin D3 had 78.4% sensitivity and 68.3% specificity in predicting T2DM complications. At a value of >158 pmol/L, Pro-NT predicted T2DM complications with 67.6% sensitivity and 56.0% specificity; (4) Conclusions: 25 (OH) Vit D3 and Pro-NT could identify T2DM patients and predict T2DM complications. More extensive research is required to adequately validate this novel perspective with a large population study.

Machine learning models for prediction of HF and CKD development in early-stage type 2 diabetes patients

Chronic kidney disease (CKD) and heart failure (HF) are the first and most frequent comorbidities associated with mortality risks in early-stage type 2 diabetes mellitus (T2DM). However, efficient screening and risk assessment strategies for identifying T2DM patients at high risk of developing CKD and/or HF (CKD/HF) remains to be established. This study aimed to generate a novel machine learning (ML) model to predict the risk of developing CKD/HF in early-stage T2DM patients. The models were derived from a retrospective cohort of 217,054 T2DM patients without a history of cardiovascular and renal diseases extracted from a Japanese claims database. Among algorithms used for the ML, extreme gradient boosting exhibited the best performance for CKD/HF diagnosis and hospitalization after internal validation and was further validated using another dataset including 16,822 patients. In the external validation, 5-years prediction area under the receiver operating characteristic curves for CKD/HF diagnosis and hospitalization were 0.718 and 0.837, respectively. In Kaplan–Meier curves analysis, patients predicted to be at high risk showed significant increase in CKD/HF diagnosis and hospitalization compared with those at low risk. Thus, the developed model predicted the risk of developing CKD/HF in T2DM patients with reasonable probability in the external validation cohort. Clinical approach identifying T2DM at high risk of developing CKD/HF using ML models may contribute to improved prognosis by promoting early diagnosis and intervention.

A prediction model to assess the risk of egfr loss in patients with type 2 diabetes and preserved kidney function: The amd annals initiative

ObjectiveTo develop and validate a model for predicting 5-year eGFR-loss in type 2 diabetes mellitus (T2DM) patients with preserved renal function at baseline. Research design and methodsA cohort of 504.532 T2DM outpatients participating to the Medical Associations of Diabetologists (AMD) Annals Initiative was splitted into the Learning and Validation cohorts, in which the predictive model was respectively developed and validated. A multivariate Cox proportional hazard regression model including all baseline characteristics was performed to identify predictors of eGFR-loss. A weight derived from regression coefficients was assigned to each variable and the overall sum of weights determined the 0 to 8-risk score. ResultsA set of demographic, clinical and laboratory parameters entered the final model. The eGFR-loss score showed a good performance in the Validation cohort. Increasing score values progressively identified a higher risk of GFR loss: a score ≥ 8 was associated with a HR of 13.48 (12.96–14.01) in the Learning and a HR of 13.45 (12.93–13.99) in the Validation cohort.The 5 years-probability of developing the study outcome was 55.9% higher in subjects with a score ≥ 8. ConclusionsIn the large AMD Annals Initiative cohort, we developed and validated an eGFR-loss prediction model to identify T2DM patients at risk of developing clinically meaningful renal complications within a 5-years time frame.

Development and validation of a nomogram based on the hospital information system for quantitative assessment of the risk of cardiocerebrovascular complications of diabetes.

BackgroundAlthough the prevention and treatment of the cardiocerebrovascular complications (CCVCs) of diabetes have been clarified, their incidence is still high. This is largely due to the lack of predictive models to objectively assess the risk of CCVC in patients with type 2 diabetes mellitus (T2DM), reducing their treatment adherence. Despite the fact that the risk factors of CCVC in T2DM patients have been identified, no prediction model for identifying T2DM patients with the risk of CCVC is available. Therefore, the aim of this study is to establish a nomogram based on hospital information system data to quantitatively assess the risk of CCVCs in T2DM patients. This model is contributed to individualized therapeutic treatments and motivating T2DM patients to adhere to lifestyle interventions.MethodsThe medical records of 1,556 T2DM patients, comprising 1,145 cases in the training cohort and 411 in the validation cohort were retrospectively analyzed. CCVCs of diabetes, including coronary heart disease, cerebral ischemia, and intracerebral hemorrhage, were extracted from the medical records. Univariate and multivariate logistic regression analyses were performed to screen the independent correlates of CCVCs from the demographic information and laboratory test data, which were utilized to establish a nomogram for predicting the risk of CCVCs in these patients. We used internal and external validation based on the training and validation cohorts to evaluate the model performance.ResultsThe incidence of CCVCs in the training cohort (26.99%) was similar to the validation cohort (25.79%). Disease duration, body mass index (BMI), systolic blood pressure (SBP), glycosylated hemoglobin (HbA1c), and uric acid (UA) levels were finally included in the established nomogram. In both the internal and external validation, the nomogram showed good discrimination [area under the curve (AUC) =0.850 and 0.825, respectively] and calibration (P=0.127 and P=0.096, respectively). Decision curve analysis showed that the nomogram produced a net benefit in both the training and validation cohorts.ConclusionsThe nomogram developed for predicting the risk of CCVC in T2DM patients may help improve treatment adherence. Further multi-center prospective investigations are required to predict the timing of CVCC in T2DM patients.

Poor Glycaemic Control and its Associated Factors among Type 2 Diabetes Mellitus Patients in Southern Part of Peninsular Malaysia: A Registry-based Study

BACKGROUND: Type 2 diabetes mellitus (T2DM) is a growing global health concern that is likely to reach a pandemic level by 2030 and is one of the leading causes of death globally. Recently, T2DM has been causing an increase in premature mortality including in developing countries.&#x0D; AIM: This study aimed to determine the prevalence and the factors associated with poor glycaemic control among Type 2 diabetes mellitus (T2DM) patients in Segamat, Malaysia. &#x0D; METHOD: The study population was selected from the National Diabetic Registry (NDR) database between June 2019 and September 2020 which included a total of 3,100 patients. General and clinical information were retrieved from the registry. Glycaemic control was categorised as good (HbA1c ≤6.5 %) or poor (HbA1c &gt;6.5%). Univariable and multivariable logistic regression were performed to assess the factors of poor glycaemic control&#x0D; RESULTS: More than half (59.2%) of the patients had poor glycaemic control. As high as 55.1% of older patients (≥60 years old) had poorer glycaemic control. Most patients with poor glycaemic control (62.0%) were obese. Multiple logistic regression analysis revealed that age (≥60 years old), ethnicity (Malay and Indian), more than 10 years of diabetes, obesity, early diabetes onset before 40 years, and dyslipidaemia were associated with poor glycaemic control.&#x0D; CONCLUSION: These findings can provide the necessary guidance for the stakeholders in identifying T2DM patients at risk of poor glycaemic control so that early preventive measures and organised care can be provided for the patients.

Anthropometric indices as novel markers of risk in type 2 diabetes mellitus (T2DM) among Nigerian adults in Zamfara State

Body mass index (BMI) has traditionally been used as an indicator of body size measure and composition. Although, other measures of adiposity of the abdomen such as waist circumference (WC), waist-hip ratio (WHR), neck circumference (NC) have been suggested as being superior to BMI in predicting disease outcome. This study was designed to compare different anthropometric variables in term of their ability to predict type 2 diabetes mellitus (T2DM). This was a case-control study in 240 participants involving 120 verified T2DM cases and 120 non-diabetics as control. Age, gender and anthropometric data were collected from each participant. Logistic regression models were used with areas under the receiver operating characteristic (AROC) curve to compare the variables predictive statistics. The AROC of WHR to identify T2DM patients was 0.678 (P&lt;0.05), with sensitivity 62.5% of and specificity of 60.8%. The AROC for average arm circumference (AAC) model is 0.649 with sensitivity of 55.8% followed by BMI model (AROC 0.635) and WC model (AROC 0.600) (P&lt;0.05). Hip circumference (HC) (AROC 0.508) and NC (AROC 0.492) models were not significant predictors of T2DM. Subjects of ≥60 years, AAC value ≥32.6 cm, BMI value ≥ 30 kg/m2, and WHR value ≥ 0.93 were at significantly (P&lt;0.05) higher odds of developing T2DM than lower subjects with lower values. There were no significant differences (P&gt;0.05) in the mean HC and NC values between the diabetic and non-diabetic subjects. The non-diabetic subjects have significantly (P&gt;0.05) higher mean height value than the diabetic subjects. Measures of generalized and central obesity were significantly associated with increased risk of developing T2DM. This study revealed that WHR can predict type 2 diabetes mellitus risk more accurately than other anthropometric measures and can thus be helpful in predicting patients with future occurrence of diabetes and providing necessary interventions

Prognostic value of non-alcoholic fatty liver disease for predicting cardiovascular events in patients with diabetes mellitus with suspected coronary artery disease: a prospective cohort study

BackgroundRisk stratification of cardiovascular events in patients with type 2 diabetes mellitus (T2DM) has not been established. Coronary artery calcium score (CACS) and non-alcoholic fatty liver disease (NAFLD) are independently associated with cardiovascular events in T2DM patients. This study examined the incremental prognostic value of NAFLD assessed by non-enhanced computed tomography (CT) in addition to CACS and Framingham risk score (FRS) for cardiovascular events in T2DM patients.MethodsThis prospective pilot study included 529 T2DM outpatients with no history of cardiovascular disease who underwent CACS measurement because of suspected coronary artery disease. NAFLD was defined on CT images as a liver:spleen attenuation ratio < 1.0. Cardiovascular events were defined as cardiovascular death, nonfatal myocardial infarction, late coronary revascularization, nonfatal stroke, or hospitalization for heart failure.ResultsAmong 529 patients (61% men, mean age 65 years), NAFLD was identified in 143 (27%). Forty-four cardiovascular events were documented during a median follow-up of 4.4 years. In multivariate Cox regression analysis, NAFLD, CACS, and FRS were associated with cardiovascular events (hazard ratios and 95% confidence intervals 5.43, 2.82–10.44, p < 0.001; 1.56, 1.32–1.86, p < 0.001; 1.23, 1.08–1.39, p = 0.001, respectively). The global χ2 score for predicting cardiovascular events increased significantly from 27.0 to 49.7 by adding NAFLD to CACS and FRS (p < 0.001). The addition of NAFLD to a model including CACS and FRS significantly increased the C-statistic from 0.71 to 0.80 (p = 0.005). The net reclassification achieved by adding CACS and FRS was 0.551 (p < 0.001).ConclusionsNAFLD assessed by CT, in addition to CACS and FRS, could be useful for identifying T2DM patients at higher risk of cardiovascular events.

Serum Adenosine Deaminase as a Useful Marker to Estimate Coronary Artery Calcification in Type 2 Diabetes Mellitus Patients

We investigated the association between serum adenosine deaminase and coronary artery calcification (CAC) in type 2 diabetes mellitus (T2DM) patients. The cross-sectional study included 459 patients with T2DM, the clinical and laboratory tests were performed, and all T2DM patients were separated into the 3 groups based on the tertile of serum adenosine deaminase levels. In the baseline data, the CAC score had statistically significant differences between the 3 groups (p < 0.001). Serum adenosine deaminase levels were positively correlated with CAC score in T2DM patients (r = 0.355, p < 0.001). The results of multiple linear regression analysis showed that serum adenosine deaminase was independent positively correlated with CAC score in T2DM patients (r = 0.255, p < 0.001). Receiver-operating characteristic curve analysis showed that area under curve was 0.750 to identify T2DM patients with CAC. Serum adenosine deaminase levels are correlated with CAC scores in T2DM patients, clinically, serum adenosine deaminase should be considered as an underlying marker to determine the severity of atherosclerosis in T2DM patients.

Generalized anxiety disorder in type 2 diabetes mellitus: prevalence and clinical characteristics.

Objective:This study investigated the prevalence of generalized anxiety disorder (GAD) in Taiwanese patients with type 2 diabetes mellitus (T2DM).Methods:This retrospective observational study was conducted with a random sample of patients from the entire population of National Health Insurance enrollees during 2000-2010 and used ICD-9-CM diagnostic codes to identify T2DM patients and GAD. The prevalence of GAD was compared between T2DM patients and the general population.Results:Between 2000 and 2010, the prevalence of GAD was significantly greater in the T2DM patients than the general population, while the increase of GAD was higher in the general population (from 0.25 to 0.63%) than among T2DM patients (from 0.81 to 1.03%). In T2DM patients, GAD was associated with female gender, a Charlson Comorbidity Index ≥ 1, diabetes mellitus duration > 9 years, and the following comorbidities: congestive heart failure, peripheral vascular disease, and depressive disorder. The prevalence of GAD among T2DM patients was negatively associated with rapid-acting insulin injection therapy and with the use of metformin and sulfonylureas.Conclusion:Since the prevalence of GAD was greater among T2DM patients than the general population, public health initiatives are needed to prevent and treat GAD in T2DM patients, specifically those with the above mentioned risk factors.

Validation of the ESC/EASD cardiovascular risk stratification model in diabetic patients

Abstract Background Recently, the European Society of Cardiology (ESC) and European Association for the Study of Diabetes (EASD) introduced a new cardiovascular (CV) risk stratification model to aid further treatment decisions in individuals with diabetes. Our study aimed to validate the ESC/EASD CV risk assessment in type 2 diabetes mellitus (T2DM) patients for the first time and to compare the prognostic significance of NT-proBNP with the ESC/EASD CV risk stratification model. Methods and results We prospectively included 2186 T2DM patients with a follow-up of 60 months. Patients were stratified according to ESC/EASD CV risk criteria, 33 (1.5%) were classified as moderate, 321 (14.9%) high and 1453 (67.6%) very high risk. Both, the ESC/EASD risk stratification model and NT-proBNP were associated with CV outcome (CV events: adj. HR: 1.47 [1.09–1.98], p=0.011 and adj. HR per IQR: 2.61 [2.27–3.01], p&amp;lt;0.0005; cardiac events: adj. HR: 1.57 [1.09–2.25], p=0.016 and adj. HR per IQR: 2.94 [2.50–3.47], p&amp;lt;0.0005; respectively) but only NT-proBNP proved to independently predict all-cause mortality (adj. HR per IQR: 2.32 [1.93–2.80], p&amp;lt;0.0005). NT-proBNP outperformed the ESC/EASD CV risk stratification model as determined by C-statistic (CV events: 0.74 vs 0.55, p&amp;lt;0.001; cardiac events: 0.76 vs 0.54, p&amp;lt;0.001; all-cause mortality: 0.73 vs 0.52, p&amp;lt;0.001; Figure 1) and net reclassification improvement of 69% for CV events, 73% for cardiac events and 67% for all-cause death (p&amp;lt;0.001 for all). Conclusion NT-proBNP measurement provides a valid tool to identify T2DM patients at risk and shows superiority in terms of risk prediction and discriminatory power compared to the ESC/EASD CV assessment. Figure 1 Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): Grant from the Austrian Science Fund

CD31+ Extracellular Vesicles From Patients With Type 2 Diabetes Shuttle a miRNA Signature Associated With Cardiovascular Complications.

Innovative biomarkers are needed to improve the management of patients with type 2 diabetes mellitus (T2DM). Blood circulating miRNAs have been proposed as a potential tool to detect T2DM complications, but the lack of tissue specificity, among other reasons, has hampered their translation to clinical settings. Extracellular vesicle (EV)-shuttled miRNAs have been proposed as an alternative approach. Here, we adapted an immunomagnetic bead-based method to isolate plasma CD31+ EVs to harvest vesicles deriving from tissues relevant for T2DM complications. Surface marker characterization showed that CD31+ EVs were also positive for a range of markers typical of both platelets and activated endothelial cells. After characterization, we quantified 11 candidate miRNAs associated with vascular performance and shuttled by CD31+ EVs in a large (n = 218) cross-sectional cohort of patients categorized as having T2DM without complications, having T2DM with complications, and control subjects. We found that 10 of the tested miRNAs are affected by T2DM, while the signature composed by miR-146a, -320a, -422a, and -451a efficiently identified T2DM patients with complications. Furthermore, another CD31+ EV-shuttled miRNA signature, i.e., miR-155, -320a, -342-3p, -376, and -422a, detected T2DM patients with a previous major adverse cardiovascular event. Many of these miRNAs significantly correlate with clinical variables held to play a key role in the development of complications. In addition, we show that CD31+ EVs from patients with T2DM are able to promote the expression of selected inflammatory mRNAs, i.e., CCL2, IL-1α, and TNFα, when administered to endothelial cells in vitro. Overall, these data suggest that the miRNA cargo of plasma CD31+ EVs is largely affected by T2DM and related complications, encouraging further research to explore the diagnostic potential and the functional role of these alterations.

Prior bariatric surgery and perioperative cardiovascular outcomes following noncardiac surgery in patients with type 2 diabetes mellitus: hint from National Inpatient Sample Database

BackgroundBoth diabetes and obesity are risk factors for perioperative major adverse events. This study aims to evaluate the association between prior bariatric surgery (prior-BS) and perioperative cardiovascular outcomes following noncardiac surgery in patients with type 2 diabetes mellitus (T2DM).MethodsWe used the National Inpatient Sample Database to identify T2DM patients undergoing major noncardiac surgery from 2006 to 2014. The primary outcome was major perioperative adverse cardiovascular and cerebrovascular events (MACCEs), which include death, acute myocardial infarction and acute ischaemic stroke. In-hospital outcomes between patients with prior BS and morbid obesity were compared using unadjusted logistic, multivariable logistic and propensity score matching analyses.ResultsA weighted of 1,526,820 patients diagnosed with T2DM who underwent noncardiac surgery were included. The rates of both prior BS and morbid obesity significantly increased during the study period (P < 0.0001). Patients with prior BS were younger, were more likely to be female, and had lower rates of cardiovascular risk factors but had higher rates of smoking, alcohol abuse, anaemia, prior venous thromboembolism and prior percutaneous coronary intervention. The incidence of MACCEs was 1.01% and 3.25% in patients with prior BS and morbid obesity, respectively. After multivariable adjustment, we found that prior BS was associated with a reduced risk of MACCEs (odds ratio [OR] = 0.71; 95% confidence interval [CI] 0.62–0.81), death (OR = 0.64, 95% CI 0.52–0.78), acute kidney injury (OR = 0.66, 95% CI 0.62–0.70) and acute respiratory failure (OR: 0.46; 95% CI 0.42–0.50).ConclusionsPrior bariatric surgery in T2DM patients undergoing noncardiac surgery is associated with a lower risk of MACCEs. Prospective studies are needed to verify the benefits of bariatric surgery in patients undergoing noncardiac surgery.

Development of risk models for major adverse chronic renal outcomes among patients with type 2 diabetes mellitus using insurance claims: a retrospective observational study

Objective: To develop and validate models allowing the prediction of major adverse chronic renal outcomes (MACRO) in patients with type 2 diabetes mellitus (T2DM) using insurance claims data.Methods: The Optum Integrated Real World Evidence Electronic Health Records and Claims de-identified database (10/01/2006–09/30/2016) was used to identify T2DM patients ≥50 years old. Risk factors were assessed over a 12-month baseline period, and MACRO were subsequently assessed until the end of data availability, continuous enrollment, or death. Separate models were built for moderate-to-severe diabetic kidney disease (DKD), end-stage renal disease (ESRD), and renal death. A random split-sample approach was employed, where 70% of the sample served for model development (training set) and the remaining 30% served for validation (testing set). C-statistics were used to assess model performance.Results: A total of 160,031 patients were included. Risk factors associated with MACRO for all models included adapted diabetes complications severity index, heart failure, anemia, diabetic nephropathy, and CKD. C-statistics ranged between 0.70 (moderate-to-severe DKD) and 0.84 (renal death) in the testing set. A substantial proportion (e.g. 88.7% for moderate-to-severe DKD) of patients predicted to be at high-risk of MACRO did not have diabetic nephropathy, proteinuria, or CKD at baseline.Conclusions: The models developed using insurance claims data could reliably predict the risk of MACRO in patients with T2DM and enabled patients at higher-risk of DKD to be identified in the absence of baseline diabetic nephropathy, CKD, or proteinuria. These models could help establish strategies to reduce the risk of MACRO in T2DM patients.

Changes in the Prescription of Glucose-Lowering Medications in Patients With Type 2 Diabetes Mellitus After a Cardiovascular Event: A Call to Action From the DATAFILE Study.

BackgroundEvidence accumulated that some glucose‐lowering medications protect against cardiovascular events (CVEs) in patients with type 2 diabetes mellitus (T2DM) and established cardiovascular disease. The present study evaluated if and how glucose‐lowering medication prescription pattern changes in T2DM after a CVE.Methods and ResultsDATAFILE (Diabetes Therapy After a Cardiovascular Event) was a retrospective multicenter study conducted at 12 diabetes mellitus specialist outpatient clinics in Italy. We identified T2DM patients with an incident CVE for whom a follow‐up visit was available after the event. We selected control T2DM patients without an incident CVE, who were matched with cases for age, sex, known diabetes mellitus duration, baseline hemoglobin A1c, kidney function, and follow‐up time. We extracted clinical variables and compared prescribed therapies at baseline and follow‐up. We included 563 patients with and 497 matched patients without an incident CVE. As expected, patients with a subsequent CVE had a higher baseline prevalence of ischemic heart disease. After a median of 9.5 months, in patients with versus those without a CVE, there was a significant increase in the prescription of beta‐blockers, loop diuretics, dual antiplatelet therapy, and, among glucose‐lowering medications, a significant decrease in metformin. Hemoglobin A1c marginally declined only in the control group, whereas low‐density lipoprotein cholesterol decreased only in patients with CVE.ConclusionsThis study highlights that occurrence of a CVE in T2DM patients did not prime the prescription of glucose‐lowering medications provided with cardiovascular protective effects, even though glucose control remained poor. These data emphasize the need to optimize the therapeutic regimen of T2DM patients with established cardiovascular disease, according to updated guidelines.

2234-PUB: A Risk Prediction Model for End-Stage Renal Disease in Patients with Type 2 Diabetes Mellitus

End-stage renal disease (ESRD) is the most severe form of chronic kidney disease (CKD) and requires dialysis or renal transplant for survival. ESRD affects more than 600,000 individuals in the United States and over 40% of cases are diabetes-related. Results from recent clinical trials consistently showed that sodium-glucose cotransporter-2 (SGLT2) inhibitors improve renal outcomes among patients with type 2 diabetes mellitus (T2DM). Cost considerations, however, emphasize the need for designing cost-effective strategies to use the medicine in this class for ESRD prevention. Therefore, the goal of this project was to develop a risk prediction model that would help identify T2DM patients at high risk to develop ESRD. The study is based on a retrospective cohort of patients with T2DM who received health care services at a large, integrated healthcare system for at least two years between 2001 and 2016. Cox regression was used to identify the strongest predictors of ESRD among 93 candidate predictors (e.g., medical history, laboratory tests) obtained from the electronic medical records. The “high-risk” group included the highest risk 10% of patients according to the final model predictors. The study sample included 58,428 patients with a mean age of 61 years. Of these, 6350 (11%) had CKD at baseline. During a mean follow-up of 6.8 years, 2208 (3.8%) patients were diagnosed with ESRD. Estimated event rates were 0.4% at 1 year and 2.7% at 5 years. The corresponding rates for the high-risk group were 2.8% and 15.1%. The predictors of ESRD included in the final model were: blood urea nitrogen, CKD, albumin, pulse pressure, hemoglobin A1c, anemia, glomerular filtration rate, and preexisting diabetes (vs. newly diagnosed) (c-statistic: 0.706). The proposed risk prediction model for ESRD demonstrates strong predictive capabilities. Applied in clinical settings, the model could help clinicians identify T2DM patients most likely to benefit from SGLT2 inhibitors to prevent or delay progression to ESRD. Disclosure D. Geba: None. J.M. Cordova: Employee; Self; Boehringer Ingelheim Pharmaceuticals, Inc. S. Shetty: None. B. Williams: None.

Supporting general practitioner-based care for poorly controlled type 2 diabetes mellitus (the DECIDE study): feasibility study and protocol for a pilot cluster randomised controlled trial

BackgroundPoorly controlled type 2 diabetes mellitus (T2DM) is associated with significant morbidity, mortality and healthcare costs. Control of T2DM can be challenging for healthcare professionals for a number of reasons, including poor concordance with medications, difficulties modifying lifestyle behaviour and also clinical inertia, which is defined as a reluctance among health professionals to intensify medications. A complex intervention, called ComputeriseD dECisIonal support for poorly controlleD typE 2 Diabetes mellitus in Irish General Practice (DECIDE), was developed, identifying T2DM patients with poor glycaemic and blood pressure control and aiming to target clinical inertia, by supporting therapeutic action, including GP-led medication intensification where appropriate. A small-scale, uncontrolled, non-randomised feasibility study highlighted the acceptability of the DECIDE intervention within Irish General Practice. This paper presents a protocol for a pilot cluster randomised controlled trial (RCT) of the DECIDE intervention.Methods/DesignThe pilot cluster RCT will involve 14 practices and 140 patients in Irish General Practice. Intervention GPs will participate in the DECIDE intervention, comprising (a) a training programme for the practices and (b) a web-based clinical decision support system supporting treatment escalation, tailored to specific patient information. Only patients who have poorly controlled T2DM (defined as HbA1c > 70 mmol/mol and/or BP > 150/95) will be included. The primary outcomes will include measures of feasibility such as recruitment and retention of practices and acceptability of the intervention and also HbA1c. Secondary outcomes will include medication intensification, blood pressure and lipids. Control GPs will continue to provide usual care. A process evaluation will be performed to determine whether the intervention is delivered as intended and treatment fidelity assessed to monitor and enhance the reliability and validity of interventions. An exploratory health economic analysis will examine the potential costs and cost effectiveness of the intervention relative to the control.DiscussionA pilot cluster RCT will establish the feasibility of a complex intervention which aims to support primary care for patients with poorly controlled T2DM in Irish General Practice.Trial registrationThe protocol for the pilot cluster RCT is registered on the ISRCTN Registry at: ISRCTN69498919.

Sleep questionnaires for the screening of obstructive sleep apnea in patients with type 2 diabetes mellitus compared with non-diabetic patients.

Several tools have been used to screen for obstructive sleep apnea (OSA). Evaluation of the predictive performance of different questionnaires is essential in patients with type 2 diabetes mellitus (T2DM) because the prevalence of OSA in this population is high. The aim of this study was to evaluate different sleep questionnaires to identify T2DM patients with OSA, and to compare the predictive values of these questionnaires with a matched sample of non-diabetic patients. The study was a retrospective study of two patients groups (n = 350 with T2DM, n = 350 without T2DM) visiting a sleep clinic and matched by age, gender, body mass index, and the apnea-hypopnea index (AHI). Symptoms of OSA and Epworth Sleepiness Scale (ESS), STOP-Bang, Berlin questionnaire, and Athens insomnia scale (AIS) scores were compared, and sleep studies were performed. Diabetic patients with OSA complained more frequently of nocturia (P = 0.025), morning headaches (P = 0.04), restless sleep (P = 0.002), and leg movements (P = 0.01) than non-diabetic patients with OSA. Most predictive values of the sleep questionnaires did not differ significantly between the two groups; however, the AIS was higher only in T2DM women (P = 0.01). In both groups, the Berlin and STOP-Bang questionnaires had the highest sensitivity. The ESS had the highest specificity in T2DM patients and the STOP and S-B questionnaires had the highest specificity in non-diabetics. The predictive performance of the questionnaires was similar in both groups, especially in the case of moderate and severe OSA.

A Risk Prediction Model for Heart Failure Hospitalization among Patients with Type 2 Diabetes

Type 2 diabetes mellitus (T2DM) is strongly associated with future cardiovascular (CV) events, including heart failure (HF) and its sequelae such as hospitalization (HFH) and death. Identifying T2DM patients at highest risk of HFH may be clinically valuable so therapeutic regimens can be optimized. Accordingly, this study aimed to develop and validate a risk prediction model for HFH among patients with T2DM. This electronic medical record- (EMR-) based, retrospective cohort study included patients with new or preexisting diagnoses of T2DM receiving health care services longitudinally through the Geisinger Health System. Study patients were assigned an index date at an office visit at least two years following their first EMR-documented encounter between 1/1/03 and 11/10/15. Candidate predictors of HFH were defined according to EMR documentation in the 2-year period prior to the index date from demographics, vital signs, medical history including diagnoses and procedures, CV-related symptoms, and laboratory tests. Cox proportional hazards regression with stepwise variable selection identified the strongest predictors of HFH. Internal validation was performed by randomly splitting the sample 1:1. Among 77,385 T2DM patients meeting study criteria, median (IQR) age of patients at the index date was 62 (52, 72) years and 49% were male. Over a mean (SD) follow-up of 5.8 (4.2) years, 2903 (3.8%) HFH occurred, and estimated HFH incidence rates at 1, 3, and 5 years after the index date were 0.5%, 1.8%, and 3.0%. The development set model containing the 15 strongest HFH predictors had a c-statistic of 0.833, and the validation set c-statistic was 0.824. The five strongest predictors were age, a history of HF, coronary artery disease, blood urea nitrogen, and HbA1c. The patients with 10% highest risk based on the model had ≥2.0% annual risk of HFH. This proposed model for HFH among T2DM demonstrated strong predictive capability and may be valuable in guiding therapeutic decision making. Disclosure B. Williams: None. P. Gandhi: Employee; Self; Boehringer Ingelheim Pharmaceuticals, Inc..

The Safety of Linagliptin vs. Other Antidiabetic Agents with Regard to the Risk of Heart Failure Hospitalization in Routine Care in the U.S.

The safety of linagliptin with regard to the risk of hospitalization for heart failure (HHF) in patients with type 2 diabetes mellitus (T2DM) as treated in routine care remains of interest. We compared the risk of HHF associated with the initiation of linagliptin vs. other antidiabetic agents. In two large commercial U.S. health insurance datasets (Optum Clinformatics and Truven MarketScan), we identified T2DM patients who initiated linagliptin, sitagliptin, saxagliptin, or sulfonylureas (SU) between 5/2011 and 9/2015 and had no previous Heart Failure (HF) history or HF treatment. We estimated hazard ratios (HR) of HHF in 3 separate propensity score matched cohorts that balanced over 130 baseline characteristics. We combined HRs via a fixed-effects meta-analysis. Mean follow-up was nearly 8 months. Linagliptin initiators had no significant difference in the risk of HHF compared with sitagliptin (HR= 0.83 [95% CI=0.58, 1.20]), saxagliptin (HR= 1.13 [0.74, 1.71]), or SU (HR= 0.71, [0.46, 1.10]). In contrast to high cardiovascular (CV) risk clinical trial populations, in a population of T2DM patients without HF history, aged 57 years on average, we did not find a difference in HHF risk between DPP-4 inhibitors with sufficient certainty. Consistent with prior studies, there may be a reduced HHF risk vs. SU; further evidence is expected from ongoing linagliptin CV outcomes trials. Disclosure E. Patorno: Research Support; Self; National Institute on Aging. Other Relationship; Self; Boehringer Ingelheim GmbH, GlaxoSmithKline plc.. M. Mahesri: None. C. Gopalakrishnan: None. K. Brodovicz: Employee; Self; Boehringer Ingelheim Pharmaceuticals, Inc. A. Meyers: Employee; Self; Boehringer Ingelheim Pharmaceuticals, Inc. D. Bartels: Employee; Self; Boehringer Ingelheim GmbH. S. Schneeweiss: Consultant; Self; WHISCON, LLC. Other Relationship; Self; Aetion, Inc.. Research Support; Self; Genentech, Inc., Bayer AG, Boehringer Ingelheim GmbH, U.S. Food and Drug Administration, Patient-Centered Outcomes Research Institute.