scholarly journals Development of a model to predict 5-year risk of severe hypoglycemia in patients with type 2 diabetes

2018 ◽  
Vol 6 (1) ◽  
pp. e000527 ◽  
Author(s):  
Lisa S Chow ◽  
Rachel Zmora ◽  
Sisi Ma ◽  
Elizabeth R Seaquist ◽  
Pamela J Schreiner
Keyword(s):  
Blood Pressure ◽  
Type 2 Diabetes ◽  
Prediction Model ◽  
Cox Regression ◽  
Severe Hypoglycemia ◽  
Diabetes Diagnosis ◽  
Reductase Inhibitors ◽  
Accord Study ◽  
Glycemic Management

ObjectiveWe constructed a predictive model of long-term risk for severe hypoglycemia (SH: hypoglycemia requiring assistance) in patients with type 2 diabetes (T2DM).Research design and methodsData from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) study (original n=10 251, n=5135 used in the current analysis), a randomized, multicenter, double 2×2 factorial design study examining the effect of glycemic, blood pressure, and lipid control on cardiovascular outcomes in patients with diagnosed T2DM, were used. Over the follow-up (3.76±1.12 years), the ACCORD participants experienced 607 incident SH events. Cox regression was used to identify the SH risk prediction model.ResultsWe identified 17 predictors—glycemic management, age, race, education, waist circumference, medications (insulin, antihypertensive, HMG-CoA reductase inhibitors, sulfonylurea, biguanide and meglitinide), years since diabetes diagnosis, history of hypoglycemia in the last week, systolic blood pressure, diastolic blood pressure, serum creatinine, and urinary albumin creatinine ratio—to construct a prediction model for SH (c-statistic=0.782). Using this information, we derived point scores to estimate the 5-year risk for SH in individual patients with T2DM. After adjusting for other variables in the model, the three strongest predictors for SH over 5 years were intensive glycemic management (HR=2.37, 95% CI 1.99 to 2.83), insulin use (HR=2.14, 95% CI 1.77 to 2.59), and antihypertensive medication use (HR=1.90, 95% CI 1.26 to 2.86).ConclusionUsing the ACCORD data, we identified attributes to predict 5-year risk of SH in patients with T2DM, which warrant evaluation in broader populations to determine applicability.

Severe Hypoglycemia and Incident Heart Failure among Adults With Type 2 Diabetes

2021 ◽  
Author(s):  
Justin B Echouffo-Tcheugui ◽  
Arnaud D Kaze ◽  
Gregg C Fonarow ◽  
Sam Dagogo-Jack
Keyword(s):  
Heart Failure ◽  
Type 2 Diabetes ◽  
Relative Risk ◽  
Cox Regression ◽  
Severe Hypoglycemia ◽  
Medical Assistance ◽  
Hazard Ratios ◽  
Accord Study

Abstract Context The effect of severe hypoglycemia on the incidence of heart failure (HF) is unclear. Objective We evaluated the association of severe hypoglycemia with incident HF among individuals with type 2 diabetes. Methods We included participants with type 2 diabetes from the Action to Control Cardiovascular Risk in Diabetes (ACCORD) study. Severe hypoglycemia episodes were assessed during the initial 24 months following randomization and defined using two methods: symptomatic, severe hypoglycemic event requiring medical assistance (first definition) or requiring any assistance (second definition). Participants without HF at baseline and during the first 24 months of the study were prospectively followed for incident HF hospitalization. Multivariable Cox regression was used to generate adjusted hazard ratios (HR) for the association of severe hypoglycemia and incident HF. Results Among 9,208 participants (mean age 63 years, 38% female, 62% White), 365 had ≥ 1 episode of severe hypoglycemic. Over a median follow-up of 3 years, there were 249 incident HF events. After multivariable adjustment for relevant confounders, participants with severe hypoglycemia requiring medical assistance had a 68% higher relative risk of incident HF (HR 1.68, 95% CI 1.06-2.66), as compared to individuals who never experienced any episode of hypoglycemia. Severe hypoglycemia requiring any assistance was also associated with a 49% higher relative risk of HF (HR 1.49, 95% CI 1.01-2.21). Conclusion In a large cohort of adults with type 2 diabetes, severe hypoglycemia was independently associated with greater risk of incident HF.

scholarly journals The development of multimorbidity during 16 years after diagnosis of type 2 diabetes

2018 ◽  
Vol 8 (1) ◽  
pp. 2235042X1880165 ◽  
Author(s):  
Sandra Pouplier ◽  
Maria Åhlander Olsen ◽  
Tora Grauers Willadsen ◽  
Håkon Sandholdt ◽  
Volkert Siersma ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Chronic Disease ◽  
Diabetes Care ◽  
Cox Regression ◽  
Neurological Diseases ◽  
Gastrointestinal Diseases ◽  
Diabetes Diagnosis ◽  
Prevalent Disease ◽  
Diabetes Patients

Objective: The aims of this study were to (1) quantify the development and composition of multimorbidity (MM) during 16 years following the diagnosis of type 2 diabetes and (2) evaluate whether the effectiveness of structured personal diabetes care differed between patients with and without MM. Research design and methods: One thousand three hundred eighty-one patients with newly diagnosed type 2 diabetes were randomized to receive either structured personal diabetes care or routine diabetes care. Patients were followed up for 19 years in Danish nationwide registries for the occurrence of outcomes. We analyzed the prevalence and degree of MM based on 10 well-defined disease groups. The effect of structured personal care in diabetes patients with and without MM was analyzed with Cox regression models. Results: The proportion of patients with MM increased from 31.6% at diabetes diagnosis to 80.4% after 16 years. The proportion of cardiovascular and gastrointestinal diseases in surviving patients decreased, while, for example, musculoskeletal, eye, and neurological diseases increased. The effect of the intervention was not different between type 2 diabetes patients with or without coexisting chronic disease. Conclusions: In general, the proportion of patients with MM increased after diabetes diagnosis, but the composition of chronic disease changed during the 16 years. We found cardiovascular and musculoskeletal disease to be the most prevalent disease groups during all 16 years of follow-up. The post hoc analysis of the intervention showed that its effectiveness was not different among patients who developed MM compared to those who continued to have diabetes alone.

scholarly journals Awaking Blood Pressure Surge and Progression to Microalbuminuria in Type 2 Normotensive Diabetic Patients

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Michelangela Barbieri ◽  
Maria Rosaria Rizzo ◽  
Ilaria Fava ◽  
Celestino Sardu ◽  
Nicola Angelico ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Type 2 Diabetes ◽  
Cox Regression ◽  
Blood Pressure Monitoring ◽  
Adult Patients ◽  
Diabetic Patients ◽  
Increased Risk ◽  
Pressure Surge

Background. We investigated the predictive value of morning blood pressure surge (MBPS) on the development of microalbuminuria in normotensive adults with a recent diagnosis of type 2 diabetes.Methods. Prospective assessments of 24-hour ambulatory blood pressure monitoring and urinary albumin excretion were performed in 377 adult patients. Multivariate-adjusted Cox regression models were used to assess hazard ratios (HRs) between baseline and changes over follow-up in MBPS and the risk of microalbuminuria. The MBPS was calculated as follows: mean systolic BP during the 2 hours after awakening minus mean systolic BP during the 1 hour that included the lowest sleep BP.Results. After a mean follow-up of 6.5 years, microalbuminuria developed in 102 patients. An increase in MBPB during follow-up was associated with an increased risk of microalbuminuria. Compared to individuals in the lowest tertile (−0.67±1.10 mmHg), the HR and 95% CI for microalbuminuria in those in the highest tertile of change (24.86±6.92 mmHg) during follow-up were 17.41 (95% CI 6.26–48.42);pfor trend <0.001. Mean SD MBPS significantly increased in those who developed microalbuminuria from a mean [SD] of 10.6[1.4]to 36.8[7.1],p<0.001.Conclusion. An increase in MBPS is associated with the risk of microalbuminuria in normotensive adult patients with type 2 diabetes.

Quality of Life, Glycemic Control, Safety and Tolerability Associated with Liraglutide or Insulin Initiation in Patients with Type 2 Diabetes in Germany: Results from the Prospective, Non-interventional LIBERTY Study

2018 ◽  
Vol 128 (03) ◽  
pp. 170-181 ◽  
Author(s):  
Rainer Lundershausen ◽  
Sabrina Müller ◽  
Mahmoud Hashim ◽  
Joachim Kienhöfer ◽  
Stefan Kipper ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Blood Pressure ◽  
Type 2 Diabetes ◽  
Body Mass Index ◽  
Glycemic Control ◽  
Body Mass ◽  
Severe Hypoglycemia ◽  
Insulin Initiation

Abstract Purpose To assess quality of life, glycemic control, and safety/tolerability associated with liraglutide versus insulin initiation in patients with type 2 diabetes in Germany. Methods Liraglutide/insulin-naïve adults with type 2 diabetes and inadequate glycemic control despite using oral antidiabetic medication were assigned to liraglutide (≤1.8 mg daily; n=878) or any insulin (n=382) according to the treating physician’s decision and followed for 52 weeks. The primary objective was to evaluate Audit of Diabetes-Dependent Quality of Life (ADDQoL) scores. Results At baseline, the liraglutide group was younger and had shorter type 2 diabetes duration, lower glycated hemoglobin (HbA1c), higher body mass index, and a lower prevalence of certain diabetes-related complications than the insulin group (all p<0.05). ADDQoL average weighted impact scores improved numerically in both groups from baseline to 52 weeks (mean difference [95% confidence interval], liraglutide vs. insulin: 0.159 [−0.023;0.340]; not significant). Changes in general wellbeing and five ADDQoL domains significantly favored liraglutide (remaining 14 domains, not significant). HbA1c reductions were greater with insulin than liraglutide (−2.0% vs. −1.2%; p<0.01); however, mean HbA1c after 52 weeks was 7.2% in both groups. Compared with insulin, liraglutide significantly decreased body mass index (−1.54 kg/m2 vs. +0.27 kg/m2; p<0.001), systolic blood pressure (−5.03 mmHg vs. −1.03 mmHg; p<0.01) and non-severe hypoglycemia (0.85% vs. 4.55% at 52 weeks; p<0.01). Adverse drug reactions were reported for<3% of patients in both groups. Conclusions Liraglutide improved certain ADDQoL components and reduced body mass index, systolic blood pressure, and non-severe hypoglycemia versus insulin. Both treatments improved glycemic control.

scholarly journals Development and Validation of a Novel Model for Predicting the 5-Year Risk of Type 2 Diabetes in Patients with Hypertension: A Retrospective Cohort Study

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Xintian Cai ◽  
Qing Zhu ◽  
Ting Wu ◽  
Bin Zhu ◽  
Xiayire Aierken ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Prediction Model ◽  
Cox Regression ◽  
Mainland China ◽  
Cardiovascular Complications ◽  
Calibration Plot ◽  
Lasso Regression ◽  
Derivation Cohort ◽  
Auc Value

Background. Hypertension is now common in China. Patients with hypertension and type 2 diabetes are prone to severe cardiovascular complications and poor prognosis. Therefore, this study is aimed at establishing an effective risk prediction model to provide early prediction of the risk of new-onset diabetes for patients with a history of hypertension. Methods. A LASSO regression model was used to select potentially relevant features. Univariate and multivariate Cox regression analyses were used to determine independent predictors. Based on the results of multivariate analysis, a nomogram of the 5-year incidence of T2D in patients with hypertension in mainland China was established. The discriminative capacity was assessed by Harrell’s C-index, AUC value, calibration plot, and clinical utility. Results. After random sampling, 1273 and 415 patients with hypertension were included in the derivation and validation cohorts, respectively. The prediction model included age, body mass index, FPG, and TC as predictors. In the derivation cohort, the AUC value and C-index of the prediction model are 0.878 (95% CI, 0.861-0.895) and 0.862 (95% CI, 0.830-0.894), respectively. In the validation cohort, the AUC value and C-index of the prediction model were 0.855 (95% CI, 0.836-0.874) and 0.841 (95% CI, 0.817-0.865), respectively. The calibration plots demonstrated good agreement between the estimated probability and the actual observation. Decision curve analysis shows that nomograms are clinically useful. Conclusion. Our nomogram can be used as a simple, affordable, reasonable, and widely implemented tool to predict the 5-year T2D risk of hypertension patients in mainland China. This application helps timely intervention to reduce the incidence of T2D in patients with hypertension in mainland China.

scholarly journals Natural Language Processing Improves Detection of Non-Severe Hypoglycemia in Medical Records versus Coding Alone in Patients with Type 2 Diabetes but does not Improve Prediction of Severe Hypoglycemia Events: An Analysis Using the Electronic Medical Record in a Large Health System

2020 ◽  
Author(s):  
Anita D. Misra-Hebert ◽  
Alex Milinovich ◽  
Alex Zajichek ◽  
Xinge Ji ◽  
Todd D. Hobbs ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Natural Language Processing ◽  
Natural Language ◽  
Prediction Model ◽  
Language Processing ◽  
Severe Hypoglycemia ◽  
C Statistic ◽  
Diagnosis Codes ◽  
Increased Risk

Objective: To determine if natural language processing (NLP) improves detection of non-severe hypoglycemia (NSH) in patients with type 2 diabetes and no NSH documentation by diagnosis codes, and to measure if NLP detection improves the prediction of future severe hypoglycemia (SH). <p>Research Design and Methods: From 2005-2017, we identified NSH events by diagnosis codes and NLP. We then built an SH prediction model. </p> <p>Results: There were 204,517 patients with type 2 diabetes and no diagnosis codes for NSH. Evidence of of NSH was found in 7035 (3.4%) using NLP. We reviewed 1200 of the NLP-detected NSH notes and confirmed 93% to have NSH. The SH prediction model (C-statistic 0.806) showed increased risk with NSH (Hazard Ratio=4.44, p<0.001). However the model with NLP did not improve SH prediction compared to diagnosis code-only NSH. </p> <p>Conclusions: Detection of NSH improved with NLP in patients with type 2 diabetes without improving SH prediction. </p>

scholarly journals Natural Language Processing Improves Detection of Non-Severe Hypoglycemia in Medical Records versus Coding Alone in Patients with Type 2 Diabetes but does not Improve Prediction of Severe Hypoglycemia Events: An Analysis Using the Electronic Medical Record in a Large Health System

2020 ◽  
Author(s):  
Anita D. Misra-Hebert ◽  
Alex Milinovich ◽  
Alex Zajichek ◽  
Xinge Ji ◽  
Todd D. Hobbs ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Natural Language Processing ◽  
Natural Language ◽  
Prediction Model ◽  
Language Processing ◽  
Severe Hypoglycemia ◽  
C Statistic ◽  
Diagnosis Codes ◽  
Increased Risk

Objective: To determine if natural language processing (NLP) improves detection of non-severe hypoglycemia (NSH) in patients with type 2 diabetes and no NSH documentation by diagnosis codes, and to measure if NLP detection improves the prediction of future severe hypoglycemia (SH). <p>Research Design and Methods: From 2005-2017, we identified NSH events by diagnosis codes and NLP. We then built an SH prediction model. </p> <p>Results: There were 204,517 patients with type 2 diabetes and no diagnosis codes for NSH. Evidence of of NSH was found in 7035 (3.4%) using NLP. We reviewed 1200 of the NLP-detected NSH notes and confirmed 93% to have NSH. The SH prediction model (C-statistic 0.806) showed increased risk with NSH (Hazard Ratio=4.44, p<0.001). However the model with NLP did not improve SH prediction compared to diagnosis code-only NSH. </p> <p>Conclusions: Detection of NSH improved with NLP in patients with type 2 diabetes without improving SH prediction. </p>

scholarly journals Exploring potential mediators of the cardiovascular benefit of dulaglutide in type 2 diabetes patients in REWIND

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Manige Konig ◽  
Matthew C. Riddle ◽  
Helen M. Colhoun ◽  
Kelley R. Branch ◽  
Charles M. Atisso ◽  
...  
Keyword(s):  
Risk Factors ◽  
Blood Pressure ◽  
Type 2 Diabetes ◽  
Systolic Blood Pressure ◽  
Repeated Measures ◽  
Cox Regression ◽  
Cox Model ◽  
Hazard Ratio ◽  
Major Adverse Cardiovascular Events

Abstract Background The REWIND trial demonstrated cardiovascular (CV) benefits to patients with type 2 diabetes and multiple CV risk factors or established CV disease. This exploratory analysis evaluated the degree to which the effect of dulaglutide on CV risk factors could statistically account for its effects on major adverse cardiovascular events (MACE) in the REWIND trial. Methods Potential mediators of established CV risk factors that were significantly reduced by dulaglutide were assessed in a post hoc analysis using repeated measures mixed models and included glycated hemoglobin (HbA1c), body weight, waist-to-hip ratio, systolic blood pressure, low-density lipoprotein (LDL), and urine albumin/creatinine ratio (UACR). These factors, for which the change in level during follow-up was significantly associated with incident MACE, were identified using Cox regression modeling. Each identified variable was then included as a covariate in the Cox model assessing the effect of dulaglutide on MACE to estimate the degree to which the hazard ratio of dulaglutide vs placebo was attenuated. The combined effect of the variables associated with attenuation was assessed by including all variables in an additional Cox model. Results Although all evaluated variables were significantly improved by treatment, only changes in HbA1c and UACR were associated with MACE and a reduction in the effect of dulaglutide on this outcome was observed. The observed hazard ratio for MACE for dulaglutide vs placebo reduced by 36.1% by the updated mean HbA1c, and by 28.5% by the updated mean UACR. A similar pattern was observed for change from baseline in HbA1c and UACR and a reduction of 16.7% and 25.4%, respectively in the hazard ratio for MACE with dulaglutide vs placebo was observed. When HbA1c and UACR were both included, the observed hazard ratio reduced by 65.4% for the updated mean and 41.7% for the change from baseline with no HbA1c-UACR interaction (P interaction = 0.75 and 0.15, respectively). Conclusions Treatment-induced improvement in HbA1c and UACR, but not changes in weight, systolic blood pressure, or LDL cholesterol, appear to partly mediate the beneficial effects of dulaglutide on MACE outcomes. These observations suggest that the proven effects of dulaglutide on cardiovascular disease benefit are partially related to changes in glycemic control and albuminuria, with residual unexplained benefit. Clinicaltrials.gov; Trial registration number: NCT01394952. URL: https://clinicaltrials.gov/ct2/show/NCT01394952

scholarly journals Visual impairment and mortality in patients with type 2 diabetes

2019 ◽  
Vol 7 (1) ◽  
pp. e000638
Author(s):  
Volkert Siersma ◽  
Rasmus Køster-Rasmussen ◽  
Christine Bruun ◽  
Niels de Fine Olivarius ◽  
Audun Brunes
Keyword(s):  
Type 2 Diabetes ◽  
Visual Acuity ◽  
Visual Impairment ◽  
Cox Regression ◽  
Independent Predictor ◽  
Diabetes Diagnosis ◽  
Newly Diagnosed ◽  
All Cause Mortality ◽  
Related Mortality

ObjectiveTo evaluate whether visual acuity impairment was an independent predictor of mortality in patients with type 2 diabetes.Research design and methodsThis is a 19-year follow-up of a cohort of 1241 patients newly diagnosed with type 2 diabetes and aged 40 years or over. Visual acuity was assessed by practicing ophthalmologists both at diabetes diagnosis and after 6 years. The logarithmic value of the visual acuity (logMAR) was the exposure. Multivariable Cox regression models were adjusted for multiple potential confounders including cardiovascular disease, and censored for potential mediators, that is, fractures/trauma. Primary outcomes were from national registers: all-cause mortality and diabetes-related mortality.ResultsVisual impairment at diabetes diagnosis was robustly associated with subsequent 6-year all-cause mortality. Per 1 unit reduced logMAR acuity the incidence rate of all-cause mortality increased with 51% (adjusted HR: 1.51; 95% CI 1.12 to 2.03) and of fractures/trauma with 59% (HR: 1.59; 95% CI 1.18 to 2.15), but visual acuity was not associated with diabetes-related mortality. After censoring for fractures/trauma, visual acuity was still an independent risk factor for all-cause mortality (HR: 1.68; 95% CI 1.23 to 2.30). In contrast, visual acuity 6 years after diabetes diagnosis was not associated with the subsequent 13 years’ incidence of any of the outcomes, as an apparent association with all-cause mortality and diabetes-related mortality was explained by confounding from comorbidity.ConclusionsVisual acuity measured by ophthalmologists in patients newly diagnosed with type 2 diabetes was an independent predictor of mortality in the short term.

scholarly journals Improvement in Glycemic and Lipid Profiles in Type 2 Diabetics with a 90-Day Ketogenic Diet

2019 ◽  
Vol 2019 ◽  
pp. 1-6 ◽  
Author(s):  
Chase M. Walton ◽  
Katelyn Perry ◽  
Richard H. Hart ◽  
Steven L. Berry ◽  
Benjamin T. Bikman
Keyword(s):  
Blood Pressure ◽  
Type 2 Diabetes ◽  
Body Weight ◽  
Blood Lipids ◽  
Dietary Intervention ◽  
Hdl Cholesterol ◽  
Diabetes Diagnosis ◽  
Type 2 Diabetics ◽  
Low Carbohydrate

Because low-carbohydrate diets are effective strategies to improve insulin resistance, the hallmark of type 2 diabetes, the purpose of reporting these clinical cases was to reveal the meaningful changes observed in 90 days of low-carbohydrate (LC) ketogenic dietary intervention in female type 2 diabetics aged 18-45. Eleven women (BMI 36.3 kg/m2) who were recently diagnosed with type 2 diabetes based on HbA1c over 6.5% (8.9%) volunteered to participate in an intensive dietary intervention to limit dietary carbohydrates to under 30 grams daily for 90 days. The main outcome was to determine the degree of change in HbA1c, while secondary outcomes included body weight, blood pressure, and blood lipids. The volunteers lost significant weight (85.7±3.2 kg to 76.7±2.8 kg) and lowered systolic (134.0±1.6 to 123.3±1.1 mmHg) and diastolic (89.9±1.3 to 82.6±1.0 mmHg) blood pressure. HbA1c dropped to 5.6%. Most blood lipids were significantly altered, including HDL cholesterol (43.1±4.4 to 52.3±3.3 mg/dl), triglycerides (177.0±19.8 to 92.1±8.7 mg/dl), and the TG : HDL ratio (4.7±0.8 to 1.9±0.2). LDL cholesterol was not significantly different. AST and ALT, plasma markers of liver health, were reported for eight patients and revealed no significant changes. These findings indicate that a short-term intervention emphasizing protein and fat at the expense of dietary carbohydrate functionally reversed the diabetes diagnosis, as defined by HbA1c. Furthermore, the intervention lowered body weight and blood pressure, while eliciting favorable changes in blood lipids.

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