Development of a prediction model for fatal and non-fatal coronary heart disease and cardiovascular disease in patients with newly diagnosed type 2 diabetes mellitus: the Basque Country Prospective Complications and Mortality Study risk engine (BASCORE)

Sex and age significantly modulate cardiovascular disease presentation in type 2 diabetes: a large population-based cohort study

Aims

We aimed to describe and compare the incidence of the first cardiovascular event and its major subtypes, coronary heart disease (CHD), cerebrovascular disease, heart failure (HF), or peripheral artery disease (PAD), according to age and sex in a population-based cohort of individuals with type 2 diabetes (T2D) from a Mediterranean region.

Material and methods

We used linked primary care electronic medical reports, pharmacy-invoicing data, and hospital admission disease registry records from the SIDIAP database, which contains linked data for 74% of the Catalonian population. We selected individuals with T2D aged 30 to 89 years free of cardiovascular disease (CVD). The primary outcome was the first presentation of CVD.

Results

The study cohort included 247,751 individuals (48.6% women, 66.8 ± 11.9 years). During a 6.99-year follow-up, the cumulative incidence of the first cardiovascular event was 23.4%. Men were at higher risk for CVD (hazard ratio [HR]: 1.47 95%CI: 1.45-1.50), CHD (HR: 1.52 95%CI: 1.47-1.57), cerebrovascular disease (HR:1.07 95%CI: 1.03-1.10) and PAD (HR: 2.30 95%CI: 2.21-2.39) than women but at a lower risk for HF (HR:0.70 95%CI: 0.68-0.73). CHD and PAD were the most frequent CVD presentations among men (28.1% and 27.5%) and HF (40.1%) in women. CHD predominated among young participants of both sexes, while HF predominated among women older than 65 and men older than 75.

Conclusions

In individuals with T2D, the overall risk and the type of first CVD manifestation largely varied by sex and age. This epidemiological evidence should be considered in clinical practice.

A literature review of quality assessment and applicability to HTA of risk prediction models of coronary heart disease in patients with diabetes

This literature review had two objectives: to identify models for predicting the risk of coronary heart diseases in patients with diabetes (DM); and to assess model quality in terms of risk of bias (RoB) and applicability for the purpose of health technology assessment (HTA). We undertook a targeted review of journal articles published in English, Dutch, Chinese, or Spanish in 5 databases from 1st January 2016 to 18th December 2022, and searched three systematic reviews for the models published after 2012. We used PROBAST (Prediction model Risk Of Bias Assessment Tool) to assess RoB, and used findings from Betts et al. 2019, which summarized recommendations and criticisms of HTA agencies on cardiovascular risk prediction models, to assess model applicability for the purpose of HTA. As a result, 71 % and 67 % models reporting C-index showed good discrimination abilities (C-index >= 0.7). Of the 26 model studies and 30 models identified, only one model study showed low RoB in all domains, and no model was fully applicable for HTA. Since the major cause of high RoB is inappropriate use of analysis method, we advise clinicians to carefully examine the model performance declared by model developers, and to trust a model if all PROBAST domains except analysis show low RoB and at least one validation study conducted in the same setting (e.g. country) is available. Moreover, since general model applicability is not informative for HTA, novel adapted tools may need to be developed.

Precision prognostics for cardiovascular disease in Type 2 diabetes: a systematic review and meta-analysis

BACKGROUND

Precision medicine has the potential to improve cardiovascular disease (CVD) risk prediction in individuals with Type 2 diabetes (T2D).

METHODS

We conducted a systematic review and meta-analysis of longitudinal studies to identify potentially novel prognostic factors that may improve CVD risk prediction in T2D. Out of 9380 studies identified, 416 studies met inclusion criteria. Outcomes were reported for 321 biomarker studies, 48 genetic marker studies, and 47 risk score/model studies.

RESULTS

Out of all evaluated biomarkers, only 13 showed improvement in prediction performance. Results of pooled meta-analyses, non-pooled analyses, and assessments of improvement in prediction performance and risk of bias, yielded the highest predictive utility for N-terminal pro b-type natriuretic peptide (NT-proBNP) (high-evidence), troponin-T (TnT) (moderate-evidence), triglyceride-glucose (TyG) index (moderate-evidence), Genetic Risk Score for Coronary Heart Disease (GRS-CHD) (moderate-evidence); moderate predictive utility for coronary computed tomography angiography (low-evidence), single-photon emission computed tomography (low-evidence), pulse wave velocity (moderate-evidence); and low predictive utility for C-reactive protein (moderate-evidence), coronary artery calcium score (low-evidence), galectin-3 (low-evidence), troponin-I (low-evidence), carotid plaque (low-evidence), and growth differentiation factor-15 (low-evidence). Risk scores showed modest discrimination, with lower performance in populations different from the original development cohort.

CONCLUSIONS

Despite high interest in this topic, very few studies conducted rigorous analyses to demonstrate incremental predictive utility beyond established CVD risk factors for T2D. The most promising markers identified were NT-proBNP, TnT, TyG and GRS-CHD, with the highest strength of evidence for NT-proBNP. Further research is needed to determine their clinical utility in risk stratification and management of CVD in T2D.

External validation of the UK prospective diabetes study (UKPDS) risk engine in patients with type 2 diabetes identified in the national diabetes program in Iran

Background

Cardiovascular diseases are the first leading cause of mortality in the world. Practical guidelines recommend an accurate estimation of the risk of these events for effective treatment and care. The UK Prospective Diabetes Study (UKPDS) has a risk engine for predicting CHD risk in patients with type 2 diabetes, but in some countries, it has been shown that the risk of CHD is poorly estimated. Hence, we assessed the external validity of the UKPDS risk engine in patients with type 2 diabetes identified in the national diabetes program in Iran.

Methods

The cohort included 853 patients with type 2diabetes identified between March 21, 2007, and March 20, 2018 in Lorestan province of Iran. Patients were followed for the incidence of CHD. The performance of the models was assessed in terms of discrimination and calibration. Discrimination was examined using the c-statistic and calibration was assessed with the Hosmer-Lemeshow χ2 statistic (HLχ2) test and a calibration plot was depicted to show the predicted risks versus observed ones.

Results

During 7464.5 person-years of follow-up 170 first Coronary heart disease occurred. The median follow-up was 8.6 years. The UKPDS risk engine showed moderate discrimination for CHD (c-statistic was 0.72 for 10-year risk) and the calibration of the UKPDS risk engine was poor (HLχ2 = 69.9, p < 0.001) and the UKPDS risk engine78% overestimated the risk of heart disease in patients with type 2 diabetes identified in the national diabetes program in Iran.

Conclusion

This study shows that the ability of the UKPDS Risk Engine to discriminate patients who developed CHD events from those who did not; was moderate and the ability of the risk prediction model to accurately predict the absolute risk of CHD (calibration) was poor and it overestimated the CHD risk. To improve the prediction of CHD in patients with type 2 diabetes, this model should be updated in the Iranian diabetic population.

Development and validation of a clinical prediction model for detecting coronary heart disease in middle-aged and elderly people: a diagnostic study

OBJECTIVE

To develop and validate a multivariate prediction model to estimate the risk of coronary heart disease (CHD) in middle-aged and elderly people and to provide a feasible method for early screening and diagnosis in middle-aged and elderly CHD patients.

METHODS

This study was a single-center, retrospective, case-control study. Admission data of 932 consecutive patients with suspected CHD were retrospectively assessed from September 1, 2020 to December 31, 2021 in the Department of Integrative Cardiology at China-Japan Friendship Hospital. A total of 839 eligible patients were included in this study, and 588 patients were assigned to the derivation set and 251 as the validation set at a 7:3 ratio. Clinical characteristics of included patients were compared between derivation set and validation set by univariate analysis. The least absolute shrinkage and selection operator (Lasso) regression analysis method was performed to avoid collinearity and identify key potential predictors. Multivariate logistic regression analysis was used to construct a clinical prediction model with identified predictors for clinical practice. Bootstrap validation was used to test performance and eventually we obtained the actual model. And the Hosmer-Lemeshow test was carried out to evaluate the goodness-fit of the constructed model. The area under curve (AUC) of receiver operating characteristic (ROC), calibration curve, decision curve analysis (DCA), and clinical impact curve (CIC) were plotted and utilized with validation set to comprehensively evaluate the predictive accuracy and clinical value of the model.

RESULTS

A total of eight indicators were identified as risk factors for the development of CHD in middle-aged and elderly people by univariate analysis. Of these candidate predictors, four key parameters were defined to be significantly related to CHD by Lasso regression analysis, including age (OR 1.034, 95% CI 1.002 ~ 1.067, P = 0.040), hemoglobin A1c (OR 1.380, 95% CI 1.078 ~ 1.768, P = 0.011), ankle-brachial index (OR 0.078, 95% CI 0.012 ~ 0.522, P = 0.009), and brachial artery flow-mediated vasodilatation (OR 0.848, 95% CI 0.726 ~ 0.990, P = 0.037). The Hosmer-Lemeshow test showed a good calibration performance of the clinical prediction model (derivation set, χ2 = 7.865, P = 0.447; validation set, χ2 = 11.132, P = 0.194). The ROCs of the nomogram in the derivation set and validation set were 0.722 and 0.783, respectively, suggesting excellent predictive power and suitable performance. The clinical prediction model presented a greater net benefit and clinical impact based on DCA and CIC analysis.

CONCLUSION

Overall, the development and validation of the multivariate model combined the laboratory and clinical parameters of patients with CHD, which could be beneficial to the individualized prediction of middle-aged and elderly people, and helped to facilitate clinical assessments and decisions during treatment and management of CHD.

Precision Prognostics for Cardiovascular Disease in Type 2 Diabetes: A Systematic Review and Meta-analysis

Background Precision medicine has the potential to improve cardiovascular disease (CVD) risk prediction in individuals with type 2 diabetes (T2D). Methods We conducted a systematic review and meta-analysis of longitudinal studies to identify potentially novel prognostic factors that may improve CVD risk prediction in T2D. Out of 9380 studies identified, 416 studies met inclusion criteria. Outcomes were reported for 321 biomarker studies, 48 genetic marker studies, and 47 risk score/model studies. Results Out of all evaluated biomarkers, only 13 showed improvement in prediction performance. Results of pooled meta-analyses, non-pooled analyses, and assessments of improvement in prediction performance and risk of bias, yielded the highest predictive utility for N-terminal pro b-type natriuretic peptide (NT-proBNP) (high-evidence), troponin-T (TnT) (moderate-evidence), triglyceride-glucose (TyG) index (moderate-evidence), Genetic Risk Score for Coronary Heart Disease (GRS-CHD) (moderate-evidence); moderate predictive utility for coronary computed tomography angiography (low-evidence), single-photon emission computed tomography (low-evidence), pulse wave velocity (moderate-evidence); and low predictive utility for C-reactive protein (moderate-evidence), coronary artery calcium score (low-evidence), galectin-3 (low-evidence), troponin-I (low-evidence), carotid plaque (low-evidence), and growth differentiation factor-15 (low-evidence). Risk scores showed modest discrimination, with lower performance in populations different from the original development cohort. Conclusions Despite high interest in this topic, very few studies conducted rigorous analyses to demonstrate incremental predictive utility beyond established CVD risk factors for T2D. The most promising markers identified were NT-proBNP, TnT, TyG and GRS-CHD, with the highest strength of evidence for NT-proBNP. Further research is needed to determine their clinical utility in risk stratification and management of CVD in T2D.

Development and validation of risk prediction models for stroke and mortality among patients with type 2 diabetes in northern China

BACKGROUND

Stroke is one of the leading causes of disability and mortality in patients with type 2 diabetes mellitus (T2DM). Risk models have been developed for predicting stroke and stroke-associated mortality among patients with T2DM. Here, we evaluated risk factors of stroke for individualized prevention measures in patients with T2DM in northern China.

METHODS

In the community-based Tianjin Chronic Disease Cohort study, 58,042 patients were enrolled between January 2014 and December 2019. We used multiple imputation (MI) to impute missing variables and univariate and multivariate Cox's proportional hazard regression to screen risk factors of stroke. Furthermore, we established and validated first-ever prediction models for stroke (Model 1 and Model 2) and death from stroke (Model 3) and evaluated their performance.

RESULTS

In the derivation and validation groups, the area under the curves (AUCs) of Models 1-3 was better at 5 years than at 8 years. The Harrell's C-index for all models was above 0.7. All models had good calibration, discrimination, and clinical net benefit. Sensitivity analysis using the MI dataset indicated that all models had good and stable prediction performance.

CONCLUSION

In this study, we developed and validated first-ever risk prediction models for stroke and death from stroke in patients with T2DM, with good discrimination and calibration observed in all models. Based on lifestyle, demographic characteristics, and laboratory examination, these models could provide multidimensional management and individualized risk assessment. However, the models developed here may only be applicable to Han Chinese.

Incremental value of risk factor variability for cardiovascular risk prediction in individuals with type 2 diabetes: results from UK primary care electronic health records

BACKGROUND

Cardiovascular disease (CVD) risk prediction models for individuals with type 2 diabetes are important tools to guide intensification of interventions for CVD prevention. We aimed to assess the added value of incorporating risk factors variability in CVD risk prediction for people with type 2 diabetes.

METHODS

We used electronic health records (EHRs) data from 83 910 adults with type 2 diabetes but without pre-existing CVD from the UK Clinical Practice Research Datalink for 2004-2017. Using a landmark-modelling approach, we developed and validated sex-specific Cox models, incorporating conventional predictors and trajectories plus variability of systolic blood pressure (SBP), total and high-density lipoprotein (HDL) cholesterol, and glycated haemoglobin (HbA1c). Such models were compared against simpler models using single last observed values or means.

RESULTS

The standard deviations (SDs) of SBP, HDL cholesterol and HbA1c were associated with higher CVD risk (P < 0.05). Models incorporating trajectories and variability of continuous predictors demonstrated improvement in risk discrimination (C-index = 0.659, 95% CI: 0.654-0.663) as compared with using last observed values (C-index = 0.651, 95% CI: 0.646-0.656) or means (C-index = 0.650, 95% CI: 0.645-0.655). Inclusion of SDs of SBP yielded the greatest improvement in discrimination (C-index increase = 0.005, 95% CI: 0.004-0.007) in comparison to incorporating SDs of total cholesterol (C-index increase = 0.002, 95% CI: 0.000-0.003), HbA1c (C-index increase = 0.002, 95% CI: 0.000-0.003) or HDL cholesterol (C-index increase= 0.003, 95% CI: 0.002-0.005).

CONCLUSION

Incorporating variability of predictors from EHRs provides a modest improvement in CVD risk discrimination for individuals with type 2 diabetes. Given that repeat measures are readily available in EHRs especially for regularly monitored patients with diabetes, this improvement could easily be achieved.

Prediction models of diabetes complications: a scoping review

BACKGROUND

Diabetes often places a large burden on people with diabetes (hereafter 'patients') and the society, that is, in part attributable to its complications. However, evidence from models predicting diabetes complications in patients remains unclear. With the collaboration of patient partners, we aimed to describe existing prediction models of physical and mental health complications of diabetes.

METHODS

Building on existing frameworks, we systematically searched for studies in Ovid-Medline and Embase. We included studies describing prognostic prediction models that used data from patients with pre-diabetes or any type of diabetes, published between 2000 and 2020. Independent reviewers screened articles, extracted data and narratively synthesised findings using established reporting standards.

RESULTS

Overall, 78 studies reported 260 risk prediction models of cardiovascular complications (n=42 studies), mortality (n=16), kidney complications (n=14), eye complications (n=10), hypoglycaemia (n=8), nerve complications (n=3), cancer (n=2), fracture (n=2) and dementia (n=1). Prevalent complications deemed important by patients such as amputation and mental health were poorly or not at all represented. Studies primarily analysed data from older people with type 2 diabetes (n=54), with little focus on pre-diabetes (n=0), type 1 diabetes (n=8), younger (n=1) and racialised people (n=10). Per complication, predictors vary substantially between models. Studies with details of calibration and discrimination mostly exhibited good model performance.

CONCLUSION

This rigorous knowledge synthesis provides evidence of gaps in the landscape of diabetes complication prediction models. Future studies should address unmet needs for analyses of complications n> and among patient groups currently under-represented in the literature and should consistently report relevant statistics. SCOPING REVIEW REGISTRATION: https://osf.io/fjubt/.

Characteristics and Clinical Course of Diabetes of the Exocrine Pancreas: A Nationwide Population-Based Cohort Study

OBJECTIVE

The natural course of diabetes of the exocrine pancreas (DEP) is not well established. We aimed to compare the risk of insulin initiation, diabetic complications, and mortality between DEP and type 2 diabetes.

RESEARCH DESIGN AND METHODS

Using the Korean National Health Insurance Service-Health Screening Cohort between 2012 and 2017, we divided patients with diabetes into those with diabetes without prior pancreatic disease (indicated type 2 diabetes, n = 153,894) and diabetes with a prior diagnosis of pancreatic disease (indicated DEP, n = 3,629). ICD-10 codes and pharmacy prescription information were used to define type 2 diabetes, DEP, and acute and chronic diabetes complications. Kaplan-Meier curves were produced to compare insulin use over time between groups. We created logistic regression models for odds of progression to diabetic complications and mortality.

RESULTS

DEP was associated with a higher risk of insulin use than type 2 diabetes (adjusted hazard ratio 1.38 at 5 years [95% CI 1.30-1.47], P < 0.0001). Individuals with DEP showed higher risks of hypoglycemia (odds ratio 1.85 [1.54-2.21], P < 0.0001), diabetic neuropathy (1.38 [1.28-1.49], P < 0.0001), nephropathy (1.38 [1.27-1.50], P < 0.0001), retinopathy (1.10 [1.01-1.20], P = 0.0347), coronary heart disease (1.59 [1.48-1.70], P < 0.0001), cerebrovascular disease (1.38 [1.28-1.49], P < 0.0001), and peripheral arterial disease (1.34 [1.25-1.44], P < 0.0001). All-cause mortality was higher in those with DEP (1.74 [1.57-1.93], P < 0.0001) than in those with type 2 diabetes.

CONCLUSIONS

DEP is more likely to require insulin therapy than type 2 diabetes. Hypoglycemia, micro- and macrovascular complications, and all-cause mortality events are higher in DEP compared with type 2 diabetes.

Cardiovascular risk in patients with type 2 diabetes: A systematic review of prediction models

AIMS

To identify all cardiovascular disease risk prediction models developed in patients with type 2 diabetes or in the general population with diabetes as a covariate updating previous studies, describing model performance and analysing both their risk of bias and their applicability METHODS: A systematic search for predictive models of cardiovascular risk was performed in PubMed. The CHARMS and PROBAST guidelines for data extraction and for the assessment of risk of bias and applicability were followed. Google Scholar citations of the selected articles were reviewed to identify studies that conducted external validations.

RESULTS

The titles of 10,556 references were extracted to ultimately identify 19 studies with models developed in a population with diabetes and 46 studies in the general population. Within models developed in a population with diabetes, only six were classified as having a low risk of bias, 17 had a favourable assessment of applicability, 11 reported complete model information, and also 11 were externally validated.

CONCLUSIONS

There exists an overabundance of cardiovascular risk prediction models applicable to patients with diabetes, but many have a high risk of bias due to methodological shortcomings and independent validations are scarce. We recommend following the existing guidelines to facilitate their applicability.

Cardiovascular risk factors associated with acute myocardial infarction and stroke in the MADIABETES cohort

We aimed to develop two models to estimate first AMI and stroke/TIA, respectively, in type 2 diabetes mellitus patients, by applying backward elimination to the following variables: age, sex, duration of diabetes, smoking, BMI, and use of antihyperglycemic drugs, statins, and aspirin. As time-varying covariates, we analyzed blood pressure, albuminuria, lipid profile, HbA1c, retinopathy, neuropathy, and atrial fibrillation (only in stroke/TIA model). Both models were stratified by antihypertensive drugs. We evaluated 2980 patients (52.8% women; 67.3 ± 11.2 years) with 24,159 person-years of follow-up. We recorded 114 cases of AMI and 185 cases of stroke/TIA. The factors that were independently associated with first AMI were age (≥ 75 years vs. < 75 years) (p = 0.019), higher HbA1c (> 64 mmol/mol vs. < 53 mmol/mol) (p = 0.003), HDL-cholesterol (0.90–1.81 mmol/L vs. < 0.90 mmol/L) (p = 0.002), and diastolic blood pressure (65–85 mmHg vs. < 65 mmHg) (p < 0.001). The factors that were independently associated with first stroke/TIA were age (≥ 75 years vs. < 60 years) (p < 0.001), atrial fibrillation (first year after the diagnosis vs. more than one year) (p = 0.001), glomerular filtration rate (per each 15 mL/min/1.73 m² decrease) (p < 0.001), total cholesterol (3.88–6.46 mmol/L vs. < 3.88 mmol/L) (p < 0.001), triglycerides (per each increment of 1.13 mmol/L) (p = 0.031), albuminuria (p < 0.001), neuropathy (p = 0.01), and retinopathy (p = 0.023).

Risk Models and Scores of Cardiovascular Disease in Patients with Diabetes Mellitus

Diabetes mellitus (DM) is an established risk factor for atherosclerotic cardiovascular disease (CVD), and patients with DM are at a two to four-fold higher cardiovascular risk, including myocardial infraction, unstable angina, stroke, and heart failure. All of the above have arisen interest in CVD preventive strategies by the use of non-invasive methods, such as risk scores. The most common approach is to consider DM as a CVD equivalent and, therefore, to treat patients with DM in a similar way to those who required secondary CVD prevention. However, this approach has been disputed as all patients with DM do not have the same risk for CVD, and since other potentially important factors within the context of DM, such as DM duration, presence of albuminuria, and comorbidities, should be taken into consideration. Thus, the second and third approach is the application of risk models that were either developed initially for the general population or designed specifically for patients with DM, respectively. This review summarizes the evidence and implications for clinical practice regarding these scores. Up to date, several models that can be applied to the diabetic population have been proposed. However, only a few meet the minimum requirement of adequate external validation. In addition, moderate discrimination and poor calibration, which might lead to inaccurate risk estimations in populations with different characteristics, have been reported. Therefore, future research is needed before recommending a specific risk model for universal clinical practice in the management of diabetes.

Predicting morbidity by local similarities in multi-scale patient trajectories

Patient Trajectories (PTs) are a method of representing the temporal evolution of patients. They can include information from different sources and be used in socio-medical or clinical domains. PTs have generally been used to generate and study the most common trajectories in, for instance, the development of a disease. On the other hand, healthcare predictive models generally rely on static snapshots of patient information. Only a few works about prediction in healthcare have been found that use PTs, and therefore benefit from their temporal dimension. All of them, however, have used PTs created from single-source information. Therefore, the use of longitudinal multi-scale data to build PTs and use them to obtain predictions about health conditions is yet to be explored. Our hypothesis is that local similarities on small chunks of PTs can identify similar patients concerning their future morbidities. The objectives of this work are (1) to develop a methodology to identify local similarities between PTs before the occurrence of morbidities to predict these on new query individuals; and (2) to validate this methodology on risk prediction of cardiovascular diseases (CVD) occurrence in patients with diabetes. We have proposed a novel formal definition of PTs based on sequences of longitudinal multi-scale data. Moreover, a dynamic programming methodology to identify local alignments on PTs for predicting future morbidities is proposed. Both the proposed methodology for PT definition and the alignment algorithm are generic to be applied on any clinical domain. We validated this solution for predicting CVD in patients with diabetes and we achieved a precision of 0.33, a recall of 0.72 and a specificity of 0.38. Therefore, the proposed solution in the diabetes use case can result of utmost utility to secondary screening.

Epidemiology and Economic Burden of Cardiovascular Disease in Patients with Type 2 Diabetes Mellitus in Spain: A Systematic Review

INTRODUCTION

Cardiovascular disease (CVD) is a leading cause of morbidity and mortality in people with type 2 diabetes mellitus (T2DM). The objectives of this systematic literature review were to identify and synthesize published data describing the epidemiology and mortality of CVD in the T2DM population and the associated economic burden.

METHODS

We conducted a systematic review searching the PubMed and MEDES databases from 2009 to 2019 using predefined selection criteria. Peer-reviewed observational studies reporting primary or secondary data on CVD prevalence, incidence, mortality, resource use and costs in patients with T2DM in Spain, written in English and Spanish, were included. Data were tabulated and summarized descriptively.

RESULTS

Of 706 articles identified, 52 were included in the review. Most studies were based on data from hospital discharge databases and registries. The reported prevalence of CVD among patients with T2DM ranged from 6.9 to 40.8%. The prevalence of coronary heart disease ranged from 4.7 to 37%, stroke from 3.5 to 19.6%, peripheral artery disease from 2.5 to 13.0%, and heart failure from 4.3 to 20.1%. In-hospital CVD mortality rates ranged from 5.6 to 10.8%. Direct costs due to CVD in hospitalized patients with T2DM were increased (> 50%) compared with patients without CVD. No studies analysed indirect costs of CVD in patients with T2DM.

CONCLUSIONS

The burden of CVD among patients with T2DM, combined with the elevated costs of care, highlights the importance of early prevention as part of integrated management of the disease to improve clinical and economic outcomes.

Quantifying the Risk Continuum for Cardiovascular Death in Adults with Type 2 Diabetes

OBJECTIVES

In type 2 diabetes (T2D), the most common causes of death are cardiovascular (CV) related, accounting for >50% of deaths in some reports. As novel diabetes therapies reduce CV death risk, identifying patients with T2D at highest CV death risk allows for cost-effective prioritization of these therapies. Accordingly, the primary goal of this study was to quantify the risk continuum for CV death in a real-world T2D population as a means to identify patients with the greatest expected benefit from cardioprotective antidiabetes therapies.

METHODS

This retrospective study included T2D patients receiving services through an integrated health-care system and used data generated through electronic medical records (EMRs). Quantifying the risk continuum entailed developing a prediction model for CV death, creating an integer risk score based on the final prediction model and estimating future CV death risk according to risk score ranking.

RESULTS

Among 59,180 patients with T2D followed for an average of 7.5 years, 15,691 deaths occurred, 6,033 (38%) of which were CV related. The EMR-based prediction model included age, established CV disease and risk factors and glycemic indices (c statistic = 0.819). The 10% highest-risk patients according to prediction model elements had an annual CV death risk of ∼5%; the 25% highest-risk patients had an annual risk of ∼2%.

CONCLUSIONS

This study incorporated a prediction modelling approach to quantify the risk continuum for CV death in T2D. Prospective application allows us to rank individuals with T2D according to their CV death risk, and may guide prioritization of novel diabetes therapies with cardioprotective properties.

Effects of health coaching on behavioral modification among adults with cardiovascular risk factors: Systematic review and meta-analysis

OBJECTIVES

This meta-analysis examined effects of health coaching on physical activities, dietary behaviors, health responsibility, stress management, and smoking behaviors among populations with cardiovascular risk factors.

METHODS

Multiple electronic databases were searched for randomized controlled trials utilizing health coaching for people with cardiovascular risk factors to lead behavioral changes. The included studies were pooled to estimate the effect size for health coaching interventions on each of the health behaviors.

RESULTS

This meta-analysis included 15 randomized trials. Motivational interviewing and education sessions were common coaching interventions with telephone calls or face-to-face contacts as the main contact methods. Health coaching for health behaviors showed small but significant effect sizes on physical activities, dietary behaviors, health responsibility, and stress management except for smoking behaviors.

CONCLUSION

The study findings support that health coaching can induce positive behavioral changes among individuals with cardiovascular risk factors. Health coaching delivered by either expert or peer coaches would be easy to apply in clinical settings.

PRACTICAL IMPLICATIONS

Health care professionals should be aware that health coaching could provide effective motivation strategies to improve compliance of those who need to initiate and maintain their health behaviors. Health coaching could be easily delivered via telephone calls, text messages, or short-term face-to-face coaching.

Chronic complications in patients with newly diagnosed type 2 diabetes: prevalence and related metabolic and clinical features: the Verona Newly Diagnosed Type 2 Diabetes Study (VNDS) 9

INTRODUCTION

We explored the presence of chronic complications in subjects with newly diagnosed type 2 diabetes referred to the Verona Diabetes Clinic. Metabolic (insulin secretion and sensitivity) and clinical features associated with complications were also investigated.

RESEARCH DESIGN AND METHODS

The comprehensive assessment of microvascular and macrovascular complications included detailed medical history, resting ECG, ultrasonography of carotid and lower limb arteries, quantitative neurological evaluation, cardiovascular autonomic tests, ophthalmoscopy, kidney function tests. Insulin sensitivity and beta-cell function were assessed by state-of-the-art techniques (insulin clamp and mathematical modeling of glucose/C-peptide curves during oral glucose tolerance test).

RESULTS

We examined 806 patients (median age years, two-thirds males), of whom prior clinical cardiovascular disease (CVD) was revealed in 11.2% and preclinical CVD in 7.7%. Somatic neuropathy was found in 21.2% and cardiovascular autonomic neuropathy in 18.6%. Retinopathy was observed in 4.9% (background 4.2%, proliferative 0.7%). Chronic kidney disease (estimated glomerular filtration rate <60 mL/min/1.73 m²) was found in 8.8% and excessive albuminuria in 13.2% (microalbuminuria 11.9%, macroalbuminuria 1.3%).Isolated microvascular disease occurred in 30.8%, isolated macrovascular disease in 9.3%, a combination of both in 9.1%, any complication in 49.2% and no complications in 50.8%.Gender, age, body mass index, smoking, hemoglobin A1c and/or hypertension were independently associated with one or more complications. Insulin resistance and beta-cell dysfunction were associated with macrovascular but not microvascular disease.

CONCLUSIONS

Despite a generally earlier diagnosis for an increased awareness of the disease, as many as ~50% of patients with newly diagnosed type 2 diabetes had clinical or preclinical manifestations of microvascular and/or macrovascular disease. Insulin resistance might play an independent role in macrovascular disease.

TRIAL REGISTRATION NUMBER

NCT01526720.

Inverse Association Between HDL (High-Density Lipoprotein) Cholesterol and Stroke Risk Among Patients With Type 2 Diabetes Mellitus

Background and Purpose- Few studies have assessed the association of HDL (high-density lipoprotein) cholesterol with stroke risk among patients with type 2 diabetes mellitus. We aimed to investigate the association of HDL cholesterol with total and type-specific stroke risk in patients with type 2 diabetes mellitus. Methods- We performed a retrospective cohort study of 27 113 blacks and 40 431 whites with type 2 diabetes mellitus. Cox proportional hazards regression models were used to estimate the association of different levels of HDL cholesterol with stroke risk. Results- During a mean follow-up period of 3.0 years, 8496 patients developed stroke (8048 ischemic and 448 hemorrhagic). Multivariable-adjusted hazard ratios across levels of HDL at baseline (<30 [reference group], 30-39.9, 40-49.9, 50-59.9, 60-69.9, 70-79.9, and ≥80 mg/dL) were 1.00, 0.86, 0.77, 0.71, 0.71, 0.77, and 0.69 ( P_trend <0.001) for total stroke, 1.00, 0.89, 0.82, 0.75, 0.78, 0.76, and 0.75 ( P_trend <0.001) for ischemic stroke, and 1.00, 0.89, 0.69, 0.66, 0.47, and 0.94 ( P_trend =0.021) for hemorrhagic stroke, respectively. When we used an updated mean value of HDL cholesterol, the inverse association of HDL cholesterol with stroke risk did not change. This inverse association was consistent among patients of different ages, races, sexes, body mass index, hemoglobin A1c levels, never and past or current smokers, and patients with and without using glucose-lowering, cholesterol-lowering, or antihypertensive agents. Conclusions- The present study found consistent inverse associations between HDL cholesterol and the risk of total, ischemic and hemorrhagic stroke among patients with type 2 diabetes mellitus.

Prognostic tools for cardiovascular disease in patients with type 2 diabetes: A systematic review and meta-analysis of C-statistics

BACKGROUND

Diabetes is associated with an increased risk for cardiovascular diseases (CVD). Risk prediction models are tools widely used to identify individuals at particularly high-risk of adverse events. Many CVD risk prediction models have been developed but their accuracy and consistency vary.

OBJECTIVE

This study reviews the literature on available CVD risk prediction models specifically developed or validated in patients with diabetes and performs a meta-analysis of C-statistics to assess and compare their predictive performance.

METHODS

The online databases and manual reference checks of all identified relevant publications were searched.

RESULTS

Fifteen CVD prediction models developed for patients with diabetes and 11 models developed in a general population but later validated in diabetes patients were identified. Meta-analysis of C-statistics showed an overall pooled C-statistic of 0.67 and 0.64 for validated models developed in diabetes patients and in general populations respectively. This small difference in the C-statistic suggests that CVD risk prediction for diabetes patients depends little on the population the model was developed in (p = 0.068).

CONCLUSIONS

The discriminative ability of diabetes-specific CVD prediction models were modest. Improvements in the predictive ability of these models are required to understand both short and long-term risk before implementation into clinical practice.

Dysbiosis signatures of gut microbiota in coronary artery disease

Gut microbiota dysbiosis has been considered to be an important risk factor that contributes to coronary artery disease (CAD), but limited evidence exists about the involvement of gut microbiota in the disease. Our study aimed to characterize the dysbiosis signatures of gut microbiota in coronary artery disease. The gut microbiota represented in stool samples were collected from 70 patients with coronary artery disease and 98 healthy controls. 16S rRNA sequencing was applied, and bioinformatics methods were used to decipher taxon signatures and function alteration, as well as the microbial network and diagnostic model of gut microbiota in coronary artery disease. Gut microbiota showed decreased diversity and richness in patients with coronary artery disease. The composition of the microbial community changed; Escherichia-Shigella [false discovery rate (FDR = 7.5*10−5] and Enterococcus (FDR = 2.08*10−7) were significant enriched, while Faecalibacterium (FDR = 6.19*10−10), Subdoligranulum (FDR = 1.63*10−6), Roseburia (FDR = 1.95*10−9), and Eubacterium rectale (FDR = 2.35*10−4) were significant depleted in the CAD group. Consistent with the taxon changes, functions such as amino acid metabolism, phosphotransferase system, propanoate metabolism, lipopolysaccharide biosynthesis, and protein and tryptophan metabolism were found to be enhanced in CAD patients. The microbial network revealed that Faecalibacterium and Escherichia-Shigella were the microbiotas that dominated in the healthy control and CAD groups, respectively. The microbial diagnostic model based on random forest also showed probability in identifying those who suffered from CAD. Our study successfully identifies the dysbiosis signature, dysfunctions, and comprehensive networks of gut microbiota in CAD patients. Thus, modulation targeting the gut microbiota may be a novel strategy for CAD treatment.

Protective effects of rutin on liver injury in type 2 diabetic db/db mice

The aim of this study was to evaluate the protective effect of rutin on the liver of type 2 diabetic mice and explore the correlation mechanism. The db/db mice, selected as the type 2 diabetes mellitus (T2DM) models, have random blood glucose (RBG) and glucose level after 2 h of oral glucose loading of more than 16.7 mmol/L. After administration of 120 mg/kg or 60 mg/kg rutin, to T2DM mice, RBG, oral glucose tolerance, alanine aminotransferase (ALT) and aspartate aminotransferase (AST) in serum, and advanced glycation end products (AGEs) in vivo and vitro of different groups were detected. The liver pathological changes were observed under light and electron microscopy. Western blotting was used to detect the protein expression of insulin receptor substrate 2 (IRS-2) and phosphorylation of phosphatidylinositol 3 kinase (PI3K) on p85, Akt on Ser473, glycogen synthase kinase 3β (GSK-3β) on Ser9, real-time quantitative PCR was used to detect IRS-2 mRNA expression. Moreover, dynamically observing the effect of rutin on the generation of AGEs in non-enzymatic protein glycosylated system, Cell Counting Kit-8 (CCK-8) method was used to detect the effect of rutin on proliferation activity of HepG2 liver cells. The results showed that RBG and glucose levels of oral glucose tolerance test (OGTT) of mice in model group were significantly higher than that of normal group, which were significantly reduced after the rutin treatment. Rutin could reduce the ALT, AST activities and AGEs level in serum and potentiate the expression of IRS-2, P-PI3K (p85), P-Akt (Ser473), P-GSK-3β (Ser9) protein and IRS-2 mRNA in the liver tissue of db/db mice. Moreover, rutin could significantly alleviate the structure disorder of liver, reduce the degeneration and necrosis of liver cells and formation of collagen fibers of db/db mice. The results in vitro also showed that rutin could obviously inhibit the generation of AGEs, and promoted the proliferation activity of high glucose-stimulating HepG2 cells. In general, the protective effect of rutin on the liver of T2DM may be mediated by facilitating signal transduction and activated state of insulin IRS-2/PI3K/Akt/GSK-3β signal pathway, promoting hepatocyte proliferation, reducing blood glucose level and generation of AGEs.

Lipid profile, cardiovascular disease and mortality in a Mediterranean high-risk population: The ESCARVAL-RISK study

INTRODUCTION

The potential impact of targeting different components of an adverse lipid profile in populations with multiple cardiovascular risk factors is not completely clear. This study aims to assess the association between different components of the standard lipid profile with all-cause mortality and hospitalization due to cardiovascular events in a high-risk population.

METHODS

This prospective registry included high risk adults over 30 years old free of cardiovascular disease (2008-2012). Diagnosis of hypertension, dyslipidemia or diabetes mellitus was inclusion criterion. Lipid biomarkers were evaluated. Primary endpoints were all-cause mortality and hospital admission due to coronary heart disease or stroke. We estimated adjusted rate ratios (aRR), absolute risk differences and population attributable risk associated with adverse lipid profiles.

RESULTS

51,462 subjects were included with a mean age of 62.6 years (47.6% men). During an average follow-up of 3.2 years, 919 deaths, 1666 hospitalizations for coronary heart disease and 1510 hospitalizations for stroke were recorded. The parameters that showed an increased rate for total mortality, coronary heart disease and stroke hospitalization were, respectively, low HDL-Cholesterol: aRR 1.25, 1.29 and 1.23; high Total/HDL-Cholesterol: aRR 1.22, 1.38 and 1.25; and high Triglycerides/HDL-Cholesterol: aRR 1.21, 1.30, 1.09. The parameters that showed highest population attributable risk (%) were, respectively, low HDL-Cholesterol: 7.70, 11.42, 8.40; high Total/HDL-Cholesterol: 6.55, 12.47, 8.73; and high Triglycerides/HDL-Cholesterol: 8.94, 15.09, 6.92.

CONCLUSIONS

In a population with cardiovascular risk factors, HDL-cholesterol, Total/HDL-cholesterol and triglycerides/HDL-cholesterol ratios were associated with a higher population attributable risk for cardiovascular disease compared to other common biomarkers.

A Point-based Mortality Prediction System for Older Adults with Diabetes

The mortality prediction models for the general diabetic population have been well established, but the corresponding elderly-specific model is still lacking. This study aims to develop a mortality prediction model for the elderly with diabetes. The data used for model establishment were derived from the nationwide adult health screening program in Taiwan in 2007-2010, from which we applied a 10-fold cross-validation method for model construction and internal validation. The external validation was tested on the MJ health screening database collected in 2004-2007. Multivariable Cox proportional hazards models were used to predict five-year mortality for diabetic patients ≥65 years. A total of 220,832 older subjects with diabetes were selected for model construction, of whom 23,241 (10.5%) died by the end of follow-up (December 31, 2011). The significant predictors retained in the final model included age, gender, smoking status, body mass index (BMI), fasting glucose, systolic and diastolic blood pressure, leukocyte count, liver and renal function, total cholesterol, hemoglobin, albumin, and uric acid. The Harrell's C in the development, internal-, and external-validation datasets were 0.737, 0.746, and 0.685, respectively. We established an easy-to-use point-based model that could accurately predict five-year mortality risk in older adults with diabetes.

Are all people with diabetes and cardiovascular risk factors or microvascular complications at very high risk? Findings from the Risk and Prevention Study

AIMS

To verify whether it is possible, in people with diabetes mellitus (DM) considered at very high cardiovascular (CV) risk, stratify this risk better and identify significant modifiable risk factor (including lifestyle habits) to help patients and clinicians improve CV prevention.

METHODS

People with DM and microvascular diseases or one or more CV risk factors (hypertension, hyperlipidemia, smoking, poor dietary habits, overweight, physical inactivity) included in the Risk and Prevention study were selected. We considered the combined endpoint of non-fatal acute myocardial infarction and stroke and CV death. A multivariate Cox proportional analysis was carried out to identify relevant predictors. We also used the RECPAM method to identify subgroups of patients at higher risk.

RESULTS

In our study, the rate of major CV events was lower than expected (5 % in 5 years). Predictors of CV events were age, male, sex, heart failure, previous atherosclerotic disease, atrial fibrillation, insulin treatment, high HbA1c, heart rate and other CV diseases while being physically active was protective. RECPAM analysis indicated that history of atherosclerotic diseases and a low BMI defined worse prognosis (HR 4.51 95 % CI 3.04-6.69). Among subjects with no previous atherosclerotic disease, men with HbA1c more than 8 % were at higher CV risk (HR 2.77; 95 % CI 1.86-4.14) with respect to women.

CONCLUSIONS

In this population, the rate of major CV events was lower than expected. This prediction model could help clinicians identify people with DM at higher CV risk and support them in achieving goals of physical activity and HbA1c.

Comparison of different vascular risk engines in the identification of type 2 diabetes patients with high cardiovascular risk

Background

Some authors consider that secondary prevention should be conducted for all DM2 patients, while others suggest that the drug preventive treatment should start or be increased depending on each patient’s individual CVR, estimated using cardiovascular or coronary risk functions to identify the patients with a higher CVR. The principal objective of this study was to assess three different cardiovascular risk prediction models in type 2 diabetes patients.

Methods

Multicentre, cross-sectional descriptive study of 3,041 patients with type 2 diabetes and no history of cardiovascular disease. The demographic, clinical, analytical, and cardiovascular risk factor variables associated with type 2 diabetes were analysed. The risk function and probability that a cardiovascular disease could occur were estimated using three risk engines: REGICOR, UKPDS and ADVANCE. A patient was considered to have a high cardiovascular risk when REGICOR ≥ 10 % or UKPDS ≥ 15 % in 10 years or when ADVANCE ≥ 8 % in 4 years.

Results

The ADVANCE and UKPDS risk engines identified a higher number of diabetic patients with a high cardiovascular risk (24.2 % and 22.7 %, respectively) compared to the REGICOR risk engine (10.2 %). The correlation using the REGICOR risk engine was low compared to UKPDS and ADVANCE (r = 0.288 and r = 0.153, respectively; p < 0.0001). The agreement values in the allocation of a particular patient to the high risk group was low between the REGICOR engine and the UKPDS and ADVANCE engines (k = 0.205 and k = 0.123, respectively; p < 0.0001) and acceptable between the ADVANCE and UKPDS risk engines (k = 0.608).

Conclusions

There are discrepancies between the general population and the type 2 diabetic patient-specific risk engines. The results of this study indicate the need for a prospective study which validates specific equations for diabetic patients in the Spanish population, as well as research on new models for cardiovascular risk prediction in these patients.