Cardiovascular risk assessment scores for people with diabetes: a systematic review

Diabetic retinopathy related homeostatic dysregulation and its association with mortality among diabetes patients: A cohort study from NHANES

AIMS

To develop a metric termed the diabetic retinopathy-related homeostatic dysregulation (DRHD) value, and estimate its association with future risk of mortality in individuals with type 2 diabetes.

METHODS

With the data of the NHANES, the biomarkers associated with DR were identified from 40 clinical parameters using LASSO regression. Subsequently, the DRHD value was constructed utilizing the Mahalanobis distance approach. In the retrospective cohortof 6420 type 2 diabetes patients, we estimated the associations between DRHD values and mortality related to all-cause, cardiovascular disease (CVD) and diabetes-specific causes using Cox proportional hazards regression models.

RESULTS

A set of 14 biomarkers associated with DR was identified for the construction of DRHD value. During an average of 8 years of follow-up, the multivariable-adjusted HRs and corresponding 95 % CIs for the highest quartiles of DRHD values were 2.04 (1.76, 2.37), 2.32 (1.78, 3.01), and 2.29 (1.72, 3.04) for all-cause, CVD and diabetes-specific mortality, respectively. Furthermore, we developed a web-based calculator for the DRHD value to enhance its accessibility and usability (https://dzwxl-drhd.streamlit.app/).

CONCLUSIONS

Our study constructed the DRHD value as a measure to assess homeostatic dysregulation among individuals with type 2 diabetes. The DRHD values exhibited potential as a prognostic indicator for retinopathy and for mortality in patients affected by type 2 diabetes.

Using Repeated Measurements to Predict Cardiovascular Risk in Patients With Type 2 Diabetes Mellitus

The QRISK cardiovascular disease (CVD) risk assessment model is not currently optimized for patients with type 2 diabetes mellitus (T2DM). We aim to identify if the abundantly available repeatedly measured data for patients with T2D improves the predictive capability of QRISK to support the decision-making process regarding CVD prevention in patients with T2DM. We identified patients with T2DM aged 25 to 85, not on statin treatment and without pre-existing CVD from the IQVIA Medical Research Data United Kingdom primary care database and then followed them up until the first diagnosis of CVD, ischemic heart disease, or stroke/transient ischemic attack. We included traditional, nontraditional risk factors and relevant treatments for our analysis. We then undertook a Cox's hazards model accounting for time-dependent covariates to estimate the hazard rates for each risk factor and calculated a 10-year risk score. Models were developed for males and females separately. We tested the performance of our models using validation data and calculated discrimination and calibration statistics. The study included 198,835 (180,143 male with 11,976 outcomes and 90,466 female with 8,258 outcomes) patients. The 10-year predicted survival probabilities for females was 0.87 (0.87 to 0.87), whereas the observed survival estimates from the Kaplan-Meier curve for all female models was 0.87 (0.86 to 0.87). The predicted and observed survival estimates for males were 0.84 (0.84 to 0.84) and 0.84 (0.83 to 0.84) respectively. The Harrell's C-index of all female models and all male models were 0.71 and 0.69 respectively. We found that including time-varying repeated measures, only mildly improved CVD risk prediction for T2DM patients in comparison to the current practice standard. We advocate for further research using time-varying data to identify if the involvement of further covariates may improve the accuracy of currently accepted prediction models.

External validation of a cardiovascular risk model for Omani patients with type 2 diabetes mellitus: a retrospective cohort study

OBJECTIVES

To externally validate a recently developed cardiovascular disease (CVD) risk model for Omanis with type 2 diabetes mellitus (T2DM).

DESIGN

Retrospective cohort study.

SETTING

Nine primary care centres in Muscat Governorate, Oman.

PARTICIPANTS

A total of 809 male and female adult Omani patients with T2DM free of CVD at baseline were selected using a systematic random sampling strategy.

OUTCOME MEASURES

Data regarding CVD risk factors and outcomes were collected from the patients' electronic medical records between 29 August 2020 and 2 May 2021. The ability of the model to discriminate CVD risk was assessed by calculating the area under the curve (AUC) of the receiver-operating characteristic curve. Calibration of the model was evaluated using a Hosmer-Lemeshow χ2 test and the Brier score.

RESULTS

The incidence of CVD events over the 5-year follow-up period was 4.6%, with myocardial infarction being most frequent (48.6%), followed by peripheral arterial disease (27%) and non-fatal stroke (21.6%). A cut-off risk value of 11.8% demonstrated good sensitivity (67.6%) and specificity (66.5%). The area under the curve (AUC) was 0.7 (95% CI 0.60 to 0.78) and the Brier score was 0.01. However, the overall mean predicted risk was greater than the overall observed risk (11.8% vs 4.6%) and the calibration graph showed a relatively significant difference between predicted and observed risk levels in different subgroups.

CONCLUSIONS

Although the model slightly overestimated the CVD risk, it demonstrated good discrimination. Recalibration of the model is required, after which it has the potential to be applied to patients presenting to diabetic care centres elsewhere in Oman.

Machine learning profiles of cardiovascular risk in patients with diabetes mellitus: the Silesia Diabetes-Heart Project

AIMS

As cardiovascular disease (CVD) is a leading cause of death for patients with diabetes mellitus (DM), we aimed to find important factors that predict cardiovascular (CV) risk using a machine learning (ML) approach.

METHODS AND RESULTS

We performed a single center, observational study in a cohort of 238 DM patients (mean age ± SD 52.15 ± 17.27 years, 54% female) as a part of the Silesia Diabetes-Heart Project. Having gathered patients' medical history, demographic data, laboratory test results, results from the Michigan Neuropathy Screening Instrument (assessing diabetic peripheral neuropathy) and Ewing's battery examination (determining the presence of cardiovascular autonomic neuropathy), we managed use a ML approach to predict the occurrence of overt CVD on the basis of five most discriminative predictors with the area under the receiver operating characteristic curve of 0.86 (95% CI 0.80-0.91). Those features included the presence of past or current foot ulceration, age, the treatment with beta-blocker (BB) and angiotensin converting enzyme inhibitor (ACEi). On the basis of the aforementioned parameters, unsupervised clustering identified different CV risk groups. The highest CV risk was determined for the eldest patients treated in large extent with ACEi but not BB and having current foot ulceration, and for slightly younger individuals treated extensively with both above-mentioned drugs, with relatively small percentage of diabetic ulceration.

CONCLUSIONS

Using a ML approach in a prospective cohort of patients with DM, we identified important factors that predicted CV risk. If a patient was treated with ACEi or BB, is older and has/had a foot ulcer, this strongly predicts that he/she is at high risk of having overt CVD.

Prognostic models for short-term annual risk of severe complications and mortality in patients living with type 2 diabetes using a national medical claim database

OBJECTIVE

Prognostic models in patients living with diabetes allow physicians to estimate individual risk based on medical records and biological results. Clinical risk factors are not always all available to evaluate these models so that they may be complemented with models from claims databases. The objective of this study was to develop, validate and compare models predicting the annual risk of severe complications and mortality in patients living with type 2 diabetes (T2D) from a national claims data.

RESEARCH DESIGN AND METHODS

Adult patients with T2D were identified in a national medical claims database through their history of treatments or hospitalizations. Prognostic models were developed using logistic regression (LR), random forest (RF) and neural network (NN) to predict annual risk of outcome: severe cardiovascular (CV) complications, other severe T2D-related complications, and all-cause mortality. Risk factors included demographics, comorbidities, the adjusted Diabetes Severity and Comorbidity Index (aDSCI) and diabetes medications. Model performance was assessed using discrimination (C-statistics), balanced accuracy, sensibility and specificity.

RESULTS

A total of 22,708 patients with T2D were identified, with mean age of 68 years and average duration of T2D of 9.7 years. Age, aDSCI, disease duration, diabetes medications and chronic cardiovascular disease were the most important predictors for all outcomes. Discrimination with C-statistic ranged from 0.715 to 0.786 for severe CV complications, from 0.670 to 0.847 for other severe complications and from 0.814 to 0.860 for all-cause mortality, with RF having consistently the highest discrimination.

CONCLUSION

The proposed models reliably predict severe complications and mortality in patients with T2D, without requiring medical records or biological measures. These predictions could be used by payers to alert primary care providers and high-risk patients living with T2D.

Trends in predicted 10-year risk for cardiovascular diseases among patients with type 2 diabetes in Thailand, from 2014 to 2018

BACKGROUND

Cardiovascular diseases (CVD) are the leading causes of death globally, including Thailand. Approximately one-tenth of Thai adults have type 2 diabetes (T2D), a significantly increasing CVD. Our study aimed to determine the trends of predicted 10-year CVD risk among patients with T2D.

METHODS

A series of hospital-based cross-sectional studies were conducted in 2014, 2015 and 2018. We included Thai patients with T2D aged 30-74-year-old without a history of CVD. The predicted 10-year risk for CVD was calculated based on Framingham Heart Study equations both with simple office-based nonlaboratory and laboratory-based. Age- and sex-adjusted means and proportions of predicted 10-year risk for CVD were calculated.

RESULTS

A total of 84,602 patients with T2D were included in the present study. The average SBP among study participants was 129.3 ± 15.7 mmHg in 2014 and rose to 132.6 ± 14.9 mmHg in 2018. Likewise, the average body mass index was 25.7 ± 4.5 kg/m² in 2014 and elevated to 26.0 ± 4.8 kg/m² in 2018. The age- and sex-adjusted mean of the predicted 10-year CVD risk (simple office-based) was 26.2% (95% CI: 26.1-26.3%) in 2014 and rose to 27.3% (95% CI: 27.2-27.4%) in 2018 (p-for trend < 0.001). While the age- and sex-adjusted mean of the predicted 10-year CVD risk (laboratory-based) ranged from 22.4-22.9% from 2014 to 2018 (p-for trend < 0.001). The age- and sex-adjusted prevalence of the high predicted 10-year CVD risk (simple office-based) was 67.2% (95% CI: 66.5-68.0%) in 2014 and significantly rose to 73.1% (95% CI: 72.4-73.7%) in 2018 (p-for trend < 0.001). Nevertheless, the age- and sex-adjusted prevalence of the high predicted 10-year CVD risk (laboratory-based) ranged from 46.0-47.4% from 2014 to 2018 (p-for trend = 0.405). However, among patients with available laboratory results, a significantly positive correlation was noted between predicted 10-year CVD risk, simple office-based and laboratory-based (r = 0.8765, p-value < 0.001).

CONCLUSION

Our study demonstrated significant rising trends in the predicated 10-year CVD risk among Thai patients with T2D. In addition, the results empowered further improved modifiable CVD risks, especially regarding high BMI and high blood pressure.

Machine learning predicts cardiovascular events in patients with diabetes: The Silesia Diabetes-Heart Project

We aimed to develop a machine learning (ML) model for predicting cardiovascular (CV) events in patients with diabetes (DM). This was a prospective, observational study where clinical data of patients with diabetes hospitalized in the diabetology center in Poland (years 2015 - 2020) were analyzed using ML. The occurrence of new CV events following discharge was collected in the follow-up time for up to 5 years and 9 months. An end-to-end ML technique which exploits the neighborhood component analysis for elaborating discriminative predictors, followed by a hybrid sampling/boosting classification algorithm, multiple logistic regression, or unsupervised hierarchical clustering was proposed. In 1735 patients with diabetes (53% female), there were 150 (8.65%) ones with a new CV event in the follow-up. Twelve most discriminative patients' parameters included coronary artery disease, heart failure, peripheral artery disease, stroke, diabetic foot disease, chronic kidney disease, eosinophil count, serum potassium level, and being treated with clopidogrel, heparin, proton pump inhibitor, and loop diuretic. Utilizing those variables resulted in the area under the receiver operating characteristic curve (AUC) ranging from 0.62 (95% Confidence Interval [CI] 0.56-0.68, p<0.01) to 0.72 (95%CI 0.66-0.77, p<0.01) across five non-overlapping test folds, whereas multiple logistic regression correctly determined 111/150 (74.00%) high-risk patients, and 989/1585 (62.40%) low-risk patients, resulting in 1100/1735 (63.40%) correctly classified patients (AUC: 0.72, 95%CI 0.66-0.77). ML algorithms can identify patients with diabetes at a high risk of new CV events based on a small number of interpretable and easy-to-obtain patients' parameters.

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.

Management of hyperglycaemia in type 2 diabetes, 2022. A consensus report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD)

The American Diabetes Association and the European Association for the Study of Diabetes convened a panel to update the previous consensus statements on the management of hyperglycaemia in type 2 diabetes in adults, published since 2006 and last updated in 2019. The target audience is the full spectrum of the professional healthcare team providing diabetes care in the USA and Europe. A systematic examination of publications since 2018 informed new recommendations. These include additional focus on social determinants of health, the healthcare system and physical activity behaviours including sleep. There is a greater emphasis on weight management as part of the holistic approach to diabetes management. The results of cardiovascular and kidney outcomes trials involving sodium-glucose cotransporter-2 inhibitors and glucagon-like peptide-1 receptor agonists, including assessment of subgroups, inform broader recommendations for cardiorenal protection in people with diabetes at high risk of cardiorenal disease. After a summary listing of consensus recommendations, practical tips for implementation are provided.

Risk-related standards of competence are a nonsense

If a person is competent to consent to a treatment, is that person necessarily competent to refuse the very same treatment? Risk relativists answer no to this question. If the refusal of a treatment is risky, we may demand a higher level of decision-making capacity to choose this option. The position is known as asymmetry. Risk relativity rests on the possibility of setting variable levels of competence by reference to variable levels of risk. In an excellent 2016 article in Journal of Medical Ethics (JME), Rob Lawlor defends asymmetry of this kind by defending risk relativity, using and developing arguments and approaches found in earlier work such as that of Wilks. He offers what we call the two-scale approach: a scale of risk is to be used to set a standard of competence on a scale of decision-making difficulty. However, can this be done in any rational way? We argue it cannot, and in this sense, and to this extent, risk relativity is a nonsense.

Management of Hyperglycemia in Type 2 Diabetes, 2022. A Consensus Report by the American Diabetes Association (ADA) and the European Association for the Study of Diabetes (EASD)

The American Diabetes Association and the European Association for the Study of Diabetes convened a panel to update the previous consensus statements on the management of hyperglycemia in type 2 diabetes in adults, published since 2006 and last updated in 2019. The target audience is the full spectrum of the professional health care team providing diabetes care in the U.S. and Europe. A systematic examination of publications since 2018 informed new recommendations. These include additional focus on social determinants of health, the health care system, and physical activity behaviors, including sleep. There is a greater emphasis on weight management as part of the holistic approach to diabetes management. The results of cardiovascular and kidney outcomes trials involving sodium-glucose cotransporter 2 inhibitors and glucagon-like peptide 1 receptor agonists, including assessment of subgroups, inform broader recommendations for cardiorenal protection in people with diabetes at high risk of cardiorenal disease. After a summary listing of consensus recommendations, practical tips for implementation are provided.

Associations of Age at Diagnosis and Duration of Diabetes With Morbidity and Mortality Among Older Adults

Importance

Older adults vary widely in age at diagnosis and duration of type 2 diabetes, but treatment often ignores this heterogeneity.

Objectives

To investigate the associations of diabetes vs no diabetes, age at diagnosis, and diabetes duration with negative health outcomes in people 50 years and older.

Design, Setting, and Participants

This cohort study included participants in the 1995 through 2018 waves of the Health and Retirement Study (HRS), a population-based, biennial longitudinal health interview survey of older adults in the US. The study sample included adults 50 years or older (n = 36 060) without diabetes at entry. Data were analyzed from June 1, 2021, to July 31, 2022.

Exposures

The presence of diabetes, specifically the age at diabetes diagnosis, was the main exposure of the study. Age at diagnosis was defined as the age when the respondent first reported diabetes. Adults who developed diabetes were classified into 3 age-at-diagnosis groups: 50 to 59 years, 60 to 69 years, and 70 years and older.

Main Outcomes and Measures

For each diabetes age-at-diagnosis group, a propensity score-matched control group of respondents who never developed diabetes was constructed. The association of diabetes with the incidence of key outcomes-including heart disease, stroke, disability, cognitive impairment, and all-cause mortality-was estimated and the association of diabetes vs no diabetes among the age-at-diagnosis case and matched control groups was compared.

Results

A total of 7739 HRS respondents developed diabetes and were included in the analysis (4267 women [55.1%]; mean [SD] age at diagnosis, 67.4 [9.9] years). The age-at-diagnosis groups included 1866 respondents at 50 to 59 years, 2834 at 60 to 69 years, and 3039 at 70 years or older; 28 321 HRS respondents never developed diabetes. Age at diagnosis of 50 to 59 years was significantly associated with incident heart disease (hazard ratio [HR], 1.66 [95% CI, 1.40-1.96]), stroke (HR, 1.64 [95% CI, 1.30-2.07]), disability (HR, 2.08 [95% CI, 1.59-2.72]), cognitive impairment (HR, 1.30 [95% CI, 1.05-1.61]), and mortality (HR, 1.49 [95% CI, 1.29-1.71]) compared with matched controls, even when accounting for diabetes duration. These associations significantly decreased with advancing age at diagnosis. Respondents with diabetes diagnosed at 70 years or older only showed a significant association with the outcome of elevated mortality (HR, 1.08 [95% CI, 1.01-1.17]).

Conclusions and Relevance

The findings of this cohort study suggest that age at diabetes diagnosis was differentially associated with outcomes and that younger age groups were at elevated risk of heart disease, stroke, disability, cognitive impairment, and all-cause mortality. These findings reinforce the clinical heterogeneity of diabetes and highlight the importance of improving diabetes management in adults with earlier diagnosis.

Tools for Assessing Cardiovascular Disease Risk Factors in Underserved Young Adult Populations: A Systematic Review

Cardiovascular disease (CVD, i.e., disease of the heart and blood vessels) is a major cause of death globally. Current assessment tools use either clinical or non-clinical factors alone or in combination to assess CVD risk. The aim of this review was to critically appraise, compare, and summarize existing non-clinically based tools for assessing CVD risk factors in underserved young adult (18–34-year-old) populations. Two online electronic databases—PubMed and Scopus—were searched to identify existing risk assessment tools, using a combination of CVD-related keywords. The search was limited to articles available in English only and published between January 2008 and January 2019. Of the 10,383 studies initially identified, 67 were eligible. In total, 5 out of the 67 articles assessed CVD risk in underserved young adult populations. A total of 21 distinct CVD risk assessment tools were identified; six of these did not require clinical or laboratory data in their estimation (i.e., non-clinical). The main non-clinically based tools identified were the Heart Disease Fact Questionnaire, the Health Beliefs Related to CVD-Perception measure, the Healthy Eating Opinion Survey, the Perception of Risk of Heart Disease Scale, and the WHO STEPwise approach to chronic disease factor surveillance (i.e., the STEPS instrument).

Effect of Physical Activity on Cardiovascular Event Risk in a Population-Based Cohort of Patients with Type 2 Diabetes

Cardiovascular disease (CVD) is the most common cause of morbidity and mortality among patients with type 2 diabetes (T2D). Physical activity (PA) is one of the few modifiable factors that can reduce this risk. The aim of this study was to estimate to what extent PA can contribute to reducing CVD risk and all-cause mortality in patients with T2D. Information from a population-based cohort including 26,587 patients with T2D from the Navarre Health System who were followed for five years was gathered from electronic clinical records. Multivariate Cox regression models were fitted to estimate the effect of PA on CVD risk and all-cause mortality, and the approach was complemented using conditional logistic regression models within a matched nested case-control design. A total of 5111 (19.2%) patients died during follow-up, which corresponds to 37.8% of the inactive group, 23.9% of the partially active group and 12.4% of the active group. CVD events occurred in 2362 (8.9%) patients, which corresponds to 11.6%, 10.1% and 7.6% of these groups. Compared with patients in the inactive group, and after matching and adjusting for confounders, the OR of having a CVD event was 0.84 (95% CI: 0.66-1.07) for the partially active group and 0.71 (95% CI: 0.56-0.91) for the active group. A slightly more pronounced gradient was obtained when focused on all-cause mortality, with ORs equal to 0.72 (95% CI: 0.61-0.85) and 0.50 (95% CI: 0.42-0.59), respectively. This study provides further evidence that physically active patients with T2D may have a reduced risk of CVD-related complications and all-cause mortality.

Framingham Risk Score Assessment in Subjects with Pre-diabetes and Diabetes: A Cross-Sectional Study in Korea

Background

This study aimed to evaluate cardiovascular risk in subjects with pre-diabetes and diabetes in Korea.

Methods

In this pan-Korean, non-interventional, cross-sectional study, data were collected from medical records of 10 hospitals between November 2013 and June 2014. Subjects (aged ≥40 years) with medical records of dysglycemia and documentation of total cholesterol level, high-density lipoprotein cholesterol level, systolic blood pressure, and smoking status in the past 6 months were included. The primary endpoint was to determine the Framingham risk score (FRS). The relationships between FRS and cardiovascular risk factors, glycated hemoglobin, and insulin usage were determined by multiple linear regression analyses.

Results

Data from 1,537 subjects with pre-diabetes (n=1,025) and diabetes (n=512) were analyzed. The mean FRS (mean±standard deviation) in subjects with pre-diabetes/diabetes was 13.72±8.77. FRS was higher in subjects with diabetes than pre-diabetes (P<0.001). FRS in men with pre-diabetes was comparable to that in women with diabetes (13.80±7.37 vs. 13.35±7.13). FRS was elevated in subjects who consumed alcohol (2.66, P=0.033) and with obesity-class II (6.10, P=0.015) among subjects with diabetes (n=199), and was elevated in patients with left ventricular hypertrophy (11.10, P=0.005), those who consumed alcohol (3.06, P=0.000), were pre-obese (3.21, P=0.002), or were obesity-class I (2.89, P=0.002) among subjects with pre-diabetes (n=306) in comparison to subjects without these coexisting risk factors.

Conclusion

Overall, Korean subjects with pre-diabetes and diabetes have an increased cardiovascular risk, which is significantly higher in those subjects with diabetes than with pre-diabetes. The present data can be used to develop measures to prevent and manage cardiovascular complications in Koreans with impaired glucose metabolism.

Development and validation of a risk score model for prediction of lower extremity arterial disease in Chinese with type 2 diabetes aged over 50 years

BACKGROUND

Lower extremity arterial disease (LEAD) is highly prevalent in people with diabetes in China, but half of cases are underdiagnosed due to diversities of clinical presentations and complexities of diagnosis approaches. The purpose of this study was to develop a risk score model for LEAD to facilitate early screening among type 2 diabetes (T2DM) patients.

METHODS

A total of 8313 participants with T2DM from the China DIA-LEAD study, a multicenter, cross-sectional epidemiological study, were selected as the training dataset to develop a risk score model for LEAD by logistic regression. The area under receiver operating characteristic curve (AUC) and bootstrapping were utilized for internal validation. A dataset of 287 participants consecutively enrolled from a teaching hospital between July 2017 and November 2017 was used as external validation for the risk score model.

RESULTS

A total of 931 (11.2%) participants were diagnosed as LEAD in the training dataset. Factors including age, current smoking, duration of diabetes, blood pressure control, low density lipoprotein cholesterol, estimated glomerular filtration rate, and coexistence of cardio and/or cerebrovascular disease correlated with LEAD in logistic regression analysis and resulted in a weighed risk score model of 0-13. A score of ≥5 was found to be the optimal cut-off for discriminating moderate-high risk participants with AUC of 0.786 (95% CI: 0.778-0.795). The bootstrapping validation showed that the AUC was 0.784. Similar performance of the risk score model was observed in the validation dataset with AUC of 0.731 (95% CI: 0.651-0.811). The prevalence of LEAD was 3.4, 12.1, and 27.6% in the low risk (total score 0-4), moderate risk (total score 5-8), and high risk (total score 9-13) groups of LEAD in the training dataset, respectively, which were 4.3, 19.6, and 30.2% in the validation dataset.

CONCLUSION

The weighed risk score model for LEAD could reliably discriminate the presence of LEAD in Chinese with T2DM aged over 50 years, which may be helpful for a precise risk assessment and early diagnosis of LEAD.

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.

Performance of the recommended ESC/EASD cardiovascular risk stratification model in comparison to SCORE and NT-proBNP as a single biomarker for risk prediction in type 2 diabetes mellitus

BACKGROUND

Recently, the European Society of Cardiology (ESC) and European Association for the Society of Diabetes (EASD) introduced a new cardiovascular disease (CVD) risk stratification model to aid further treatment decisions in individuals with diabetes. Our study aimed to investigate the prognostic performance of the ESC/EASD risk model in comparison to the Systematic COronary Risk Evaluation (SCORE) risk model and N-terminal pro-B-type natriuretic peptide (NT-proBNP) in an unselected cohort of type 2 diabetes mellitus (T2DM).

METHODS AND RESULTS

A total of 1690 T2DM patients with a 10-year follow up for fatal CVD and all-cause death and a 5-year follow up for CVD and all-cause hospitalizations were analyzed. According to ESC/EASD risk criteria 25 (1.5%) patients were classified as moderate, 252 (14.9%) high, 1125 (66.6%) very high risk and 288 (17.0%) were not classifiable. Both NT-proBNP and SCORE risk model were associated with 10-year CVD and all-cause death and 5-year CVD and all-cause hospitalizations while the ESC/EASD model was only associated with 10-year all-cause death and 5-year all-cause hospitalizations. NT-proBNP and SCORE showed significantly higher C-indices than the ESC/EASD risk model for CVD death [0.80 vs. 0.53, p < 0.001; 0.64 vs. 0.53, p = 0.001] and all-cause death [0.73, 0.66 vs. 0.52, p < 0.001 for both]. The performance of SCORE improved in a subgroup without CVD aged 40-64 years compared to the unselected cohort, while NT-proBNP performance was robust across all groups.

CONCLUSION

The new introduced ESC/EASD risk stratification model performed limited compared to SCORE and single NT-proBNP assessment for predicting 10-year CVD and all-cause fatal events in individuals with T2DM.

Severity of Diabetic Retinopathy and the Risk of Future Cerebrovascular Disease, Cardiovascular Disease, and All-Cause Mortality

PURPOSE

To determine the relationship between the severity of diabetic retinopathy and the future risk of cerebrovascular accident (CVA), myocardial infarction (MI), congestive heart failure (CHF), and all-cause mortality in patients with type 2 diabetes mellitus.

DESIGN

Retrospective cohort study.

PARTICIPANTS

Patients with type 2 diabetes who underwent diabetic retinopathy screening via fundus photography.

METHODS

The relationship between retinopathy status and the 5-year risk of first-time CVA, MI, CHF, and all-cause mortality was investigated using multivariate Cox proportional hazards regressions that controlled for age, gender, race or ethnicity, hemoglobin A1c, duration of diabetes, high-density lipoprotein level, low-density lipoprotein level, history of hypertension, systolic blood pressure, diastolic blood pressure, tobacco use, statin use, body mass index, urine microalbumin-to-creatinine ratio, and estimated glomerular filtration rate.

MAIN OUTCOME MEASURES

Five-year risk of first-time CVA, MI, CHF, and all-cause mortality.

RESULTS

Seventy-seven thousand three hundred seventy-six patients were included in this study. The average age was 59.8 years with 53.6% male, 31.2% non-Hispanic White, and 41.4% Hispanic patients. Diabetic retinopathy was significantly associated with all outcomes on multivariate analysis. Compared with patients with no retinopathy, those with minimal nonproliferative diabetic retinopathy (NPDR) had a higher risk of CVA (hazard ratio [HR], 1.31; 95% confidence interval [CI], 1.18-1.46), MI (HR, 1.30; 95% CI, 1.15-1.46), CHF (HR, 1.29; 95% CI, 1.19-1.40), and death (HR, 1.15; 95% CI, 1.05-1.25). Similarly, patients with moderate to severe NPDR had a higher risk of each outcome (CVA: HR, 1.56; 95% CI, 1.29-1.89; MI: HR, 1.92; 95% CI, 1.57-2.34; CHF: HR, 1.90; 95% CI, 1.66-2.18, and death: HR, 1.55; 95% CI, 1.32-1.82), as did patients with proliferative diabetic retinopathy (CVA: HR, 2.53; 95% CI, 1.84-3.48; MI: HR, 1.89; 95% CI, 1.26-2.83; CHF: HR, 1.96; 95% CI, 1.47-2.59; and death: HR, 1.87; 95% CI, 1.36-2.56).

CONCLUSIONS

Diabetic retinopathy is significantly associated with future risk of CVA, MI, CHF, and death, with higher degrees of retinopathy appearing to carry a heightened risk for each outcome. Retinal information may provide valuable insights into patients' risk of future vascular disease and death.

Practical cardiovascular risk calculator for asymptomatic patients with type 2 diabetes mellitus: PRECISE-DM risk score

BACKGROUND

Obstructive coronary artery disease (OCAD) is a significant predictor of adverse clinical events in asymptomatic patients with type 2 diabetes mellitus (T2DM).

HYPOTHESIS

We sought to develop an easy-to-use risk scoring system to predict OCAD and long-term clinical outcome in asymptomatic patients with T2DM (PRECISE-DM).

METHODS

A total of 2799 asymptomatic patients with T2DM and no prior coronary disease were consecutively enrolled. OCAD was defined as ≥50% coronary artery stenosis on coronary computed tomography angiography (CCTA). A new risk scoring system was developed in 933 patients undergoing CCTA (derivation cohort) and its performance to predict OCAD and major adverse cardiac and cerebrovascular event (MACCE) was compared with other risk estimates. The scoring system was externally validated in 1899 patients not undergoing CCTA (validation cohort).

RESULTS

The PRECISE-DM scoring system was created using seven variables that were associated with increased risk of OCAD, with scores ranging from 0 to 9. The scoring system predicted presence of OCAD with a C-statistic of 0.680 and risk of MACCE with a C-statistic of 0.708. The UKPDS risk engine and the Framingham risk score showed unreliable performance in prediction of OCAD (C-statistics 0.531 and 0.577, respectively). Calcium score was highly predictive for OCAD (C-statistic 0.825) but showed only modest accuracy in predicting MACCE (C-statistic 0.675). In the external validation cohort, the PRECISE-DM score showed acceptable discrimination for prediction of MACCE (C-statistic 0.707).

CONCLUSIONS

The PRECISE-DM scoring system accurately predicted presence of OCAD and risk of MACCE in asymptomatic patients with T2DM.

Heart sparing radiotherapy in breast cancer: the importance of baseline cardiac risks

BACKGROUND

Patients with left-sided breast cancer have an increased risk of cardiovascular disease (CVD) after radiotherapy (RT). While the awareness of cardiac toxicity has increased enormously over the last decade, the role of individual baseline cardiac risks has not yet been systematically investigated. Aim of the present study was to evaluate the impact of baseline CVD risks on radiation-induced cardiac toxicity.

METHODS

Two hundred ten patients with left-sided breast cancer treated in the prospective Save-Heart Study using a deep inspiration breath-hold (DIBH) technique were analysed regarding baseline risk factors for CVD. Three frequently used prediction tools (Procam, Framingham and Reynolds score) were applied to evaluate the individual CVD risk profiles. Moreover, 10-year CVD excess absolute risks (EAR) were estimated using the individual mean heart dose (MHD) of treatment plans in free breathing (FB) and DIBH.

RESULTS

The individual baseline CVD risk factors had a strong impact on the 10-year cumulative CVD risk. The mean baseline risks of the non-diabetic cohort (n = 200) ranged from 3.11 to 3.58%, depending on the risk estimation tool. A large number of the non-diabetic patients had a very low 10-year CVD baseline risk of ≤1%; nevertheless, 8-9% of patients reached ≥10% baseline 10-year CVD risk. In contrast, diabetic patients (n = 10) had significantly higher baseline CVD risks (range: 11.76-24.23%). The mean 10-year cumulative risk (Framingham score) following RT was 3.73% using the DIBH-technique (MHD:1.42Gy) and 3.94% in FB (MHD:2.33Gy), after adding a 10-year-EAR of + 0.34%(DIBH) and + 0.55%(FB) to the baseline risks, respectively. Smoking status was one of the most important and modifiable baseline risk factors. After DIBH-RT, the 182 non-smoking patients had a mean 10-year cumulative risk of 3.55% (3.20% baseline risk, 0.35% EAR) as compared to 6.07% (5.60% baseline risk, 0.47% EAR) for the 28 smokers.

CONCLUSION

In the present study, all CVD prediction tools showed comparable results and could easily be integrated into daily clinical practice. A systematic evaluation and screening helps to identify high-risk patients who may benefit from primary prevention. This could result in an even higher benefit than from heart-sparing irradiation techniques alone.

Associations of a metabolic syndrome severity score with coronary heart disease and diabetes in fasting vs. non-fasting individuals

BACKGROUND AND AIMS

Many traditional assessments of risk for coronary heart disease (CHD) and diabetes require laboratory studies performed after an 8-h fast. We assessed whether metabolic-syndrome (MetS) severity would remain linked to future CHD and diabetes even when assessed from non-fasting samples.

METHODS AND RESULTS

Participants in the Atherosclerosis Risk in Communities study were assessed at 4 visits and followed for 20-years of adjudicated CHD outcomes. We used Cox proportional-hazard models (for 20-year CHD outcomes) and logistic regression (for 9-year diabetes outcomes) to compare incident disease risk associated with a race/ethnicity-specific MetS-severity Z-score (MetS-Z) calculated in participants who were fasting (≥8 h) or non-fasting. All analyses were adjusted for sex, race, education, income and smoking. MetS Z-scores were overall similar between participants who were always fasting vs. those non-fasting at Visits 1-3 (all values -0.1 to 0.4), while MetS-Z for participants who were non-fasting at Visit-4 were higher at each visit. Baseline MetS-Z was linked to future CHD when calculated from both fasting and non-fasting measurements, with hazard ratio (HR) for fasting MetS-Z 1.53 (95% confidence interval [CI] 1.42, 1.66) and for non-fasting 1.28 (CI 1.08, 1.51). MetS-Z at Visit-1 also remained linked to future diabetes when measured from non-fasting samples, with odds ratio for fasting MetS-Z 3.10 (CI 2.88, 3.35) and for non-fasting 1.92 (CI 1.05, 3.51).

CONCLUSIONS

MetS-Z remained linked to future CHD and diabetes when assessed from non-fasting samples. A score such as this may allow for identification of at-risk individuals and serve as a motivation toward interventions to reduce risk.

Assessing 10-Year Cardiovascular Disease Risk in Malaysians With Type 2 Diabetes Mellitus: Framingham Cardiovascular Versus United Kingdom Prospective Diabetes Study Equations

In this study, we evaluated the performance of the Framingham cardiovascular disease (CVD) and the United Kingdom Prospective Diabetes Study (UKPDS) risk equations to predict the 10-year CVD risk among type 2 diabetes mellitus (T2DM) patients in Malaysia. T2DM patients (n = 660) were randomly selected, and their 10-year CVD risk was calculated using both the Framingham CVD and UKPDS risk equations. The performance of both equations was analyzed using discrimination and calibration analyses. The Framingham CVD, UKPDS coronary heart disease (CHD), UKPDS Fatal CHD, and UKPDS Stroke equations have moderate discrimination (area under the receiver operating characteristic [aROC] curve = 0.594-0.709). The UKPDS Fatal Stroke demonstrated a good discrimination (aROC curve = 0.841). The Framingham CVD, UKPDS Stroke, and UKPDS Fatal Stroke equations showed good calibration ( P = .129 to .710), while the UKPDS CHD and UKPDS Fatal CHD are poorly calibrated ( P = .035; P = .036). The UKPDS is a better prediction equation of the 10-year CVD risk among T2DM patients compared with the Framingham CVD equation.

Predicting the risk of stroke among patients with type 2 diabetes: a systematic review and meta-analysis of C-statistics

OBJECTIVE

Stroke is a major cause of disability and death worldwide. People with diabetes are at a twofold to fivefold increased risk for stroke compared with people without diabetes. This study systematically reviews the literature on available stroke prediction models specifically developed or validated in patients with diabetes and assesses their predictive performance through meta-analysis.

DESIGN

Systematic review and meta-analysis.

DATA SOURCES

A detailed search was performed in MEDLINE, PubMed and EMBASE (from inception to 22 April 2019) to identify studies describing stroke prediction models.

ELIGIBILITY CRITERIA

All studies that developed stroke prediction models in populations with diabetes were included.

DATA EXTRACTION AND SYNTHESIS

Two reviewers independently identified eligible articles and extracted data. Random effects meta-analysis was used to obtain a pooled C-statistic.

RESULTS

Our search retrieved 26 202 relevant papers and finally yielded 38 stroke prediction models, of which 34 were specifically developed for patients with diabetes and 4 were developed in general populations but validated in patients with diabetes. Among the models developed in those with diabetes, 9 reported their outcome as stroke, 23 reported their outcome as composite cardiovascular disease (CVD) where stroke was a component of the outcome and 2 did not report stroke initially as their outcome but later were validated for stroke as the outcome in other studies. C-statistics varied from 0.60 to 0.92 with a median C-statistic of 0.71 (for stroke as the outcome) and 0.70 (for stroke as part of a composite CVD outcome). Seventeen models were externally validated in diabetes populations with a pooled C-statistic of 0.68.

CONCLUSIONS

Overall, the performance of these diabetes-specific stroke prediction models was not satisfactory. Research is needed to identify and incorporate new risk factors into the model to improve models' predictive ability and further external validation of the existing models in diverse population to improve generalisability.

Volumes of coronary plaque disease in relation to body mass index, waist circumference, truncal fat mass and epicardial adipose tissue in patients with type 2 diabetes mellitus and controls

OBJECTIVES

Coronary atherosclerosis in patients with type 2 diabetes mellitus may be promoted by regional fat distribution. We investigated the association between anthropometric measures of obesity, truncal fat mass, epicardial adipose tissue and coronary atherosclerosis in asymptomatic patients and matched controls.

METHODS

We examined 44 patients and 59 controls [mean (standard deviation) age 64.4 ± 9.9 vs 61.8 ± 9.7, male 50% vs 47%, diabetes duration mean (standard deviation) 7.7 ± 1.5] with coronary computed tomography angiography. Coronary plaques were quantified as total, calcified, non-calcified and low-density non-calcified plaque volumes (mm³). Regional fat distribution was assessed by dual-energy X-ray absorptiometry, body mass index (kg/m²), waist circumference (cm) and epicardial fat volume (mm³). Endothelial function and systemic inflammation were evaluated by peripheral arterial tonometry (log transformed Reactive Hyperemia Index) and C-reactive protein (mg/L).

RESULTS

Body mass index and waist circumference (p < 0.02) were associated with coronary plaque volumes. Body mass index was associated with low-density non-calcified plaque volume after adjustment for age, sex and diabetes status (p < 0.01). Truncal fat mass (p > 0.51), waist circumference (p > 0.06) and epicardial adipose tissue (p > 0.17) were not associated with coronary plaque volumes in adjusted analyses.

CONCLUSION

Body mass index is associated with coronary plaque volumes in diabetic as well as non-diabetic individuals.

Cardiovascular Risk, Risk Knowledge, and Related Factors in Patients With Type 2 Diabetes

Individuals with diabetes must be informed about cardiovascular diseases (CVDs), which is the most important cause of mortality of diabetes, and the interventions should be planned according to their risk status. The aim of this study was to investigate cardiovascular risk, risk knowledge, and related factors in patients with type 2 diabetes. A total of 188 participants were included in this descriptive study. Data were collected using Heart Disease Fact Questionnaire (HDFQ) and Systematic Coronary Risk Evaluation (SCORE) Calculator. Spearman test and multiple regression analysis were used for statistical analysis. Participants did not have sufficient knowledge related to CVD risk factors, and they were in the moderate CVD risk group. CVD risk was lower in subjects with high level of knowledge regarding CVD risk and lower duration of diabetes. Our findings highlight the need for interventions related to CVD, which can reduce its risk. These interventions can be specifically targeted at individuals with advanced age, a long duration of diabetes, low education level, and decreased metabolic control.

Cardiovascular Risk Estimated by UKPDS Risk Engine Algorithm in Diabetes

Since there is a high prevalence of type 2 diabetes mellitus (DM2), as well as CVD in Montenegro, we aimed to estimate CVD risk by United Kingdom Prospective Diabetes Study (UKPDS) risk engine algorithm in individuals with DM2. Furthermore, we aimed to explore whether non-traditional biomarker such as high sensitivity C-reactive protein (hsCRP) is superior for CVD risk prediction over old traditional risk factors. A total of 180 participants with DM2 (of them 50% females) were included in the current cross-sectional study. Biochemical and anthropometric parameters, and blood pressure were obtained. More males than females were classified at high UKPDS risk category (p<0.001). Also, about one third of diabetic patients (29.4%) were classified into the high-risk category. In multivariate regression analysis, triglycerides [Odds ratio (OR) =1.703, p=0.001] and creatinine concentration (OR=1.040, p<0.001) were independent predictors of CVD risk, whereas hsCRP was not correlated with CVD risk. HsCRP is not superior for CVD risk prediction by UKPDS risk engine algorithm over high triglyceride and creatinine levels in diabetic population, which suggests that the old traditional markers must not be underestimated when examining CVD risk in population with diabetes.

Cardiovascular risk assessment tools: A scoping review

OBJECTIVES

The objective of this review was to describe cardiovascular risk (CVR) assessment methods and to identify evidence-based practice recommendations when dealing with population at risk of developing cardiovascular diseases.

REVIEW METHODS AND DATA SOURCES

A literature review following the Arksey and O'Malley scoping review methodology was conducted. By using appropriate key terms, literature searches were conducted in PubMed, SciELO, Cochrane Library, Dialnet, ENFISPO, Medigraphic, ScienceDirect, Cuiden, and Lilacs databases. A complementary search on websites related to the area of interest was conducted. Articles published in English or Spanish in peer-review journals between 2010 and 2017. Critical appraisal for methodological quality was conducted. Data was extracted using ad-hoc tables and qualitatively synthesized.

RESULTS

After eliminating duplicates, 55325 records remained, and 1432 records were selected for screening. Out of these, 88 full-text articles were selected for eligibility criteria, and finally, 67 studies were selected for this review, and 25 studies were selected for evidence synthesis. In total, 23 CVR assessment tools have been identified, pioneered by the Framingham study. Qualitative findings were grouped into four thematic areas: assessment tools and scores, CVR indicators, comparative models, and evidence-based recommendations.

CONCLUSIONS

It is necessary to adapt the instruments to the epidemiological reality of the population. The most appropriate way to estimate CVR is to choose the assessment tool that best suits individual conditions, accompanied by a comprehensive assessment of the patient. More research is required to determine a single, adequate, and reliable tool.

Serum lipid management in patients with type 1 and type 2 diabetes: a hospital-based cohort study

INTRODUCTION

Serum lipid management is important for patients with diabetes; however, it has not been examined in our specialized diabetes clinic.

AIMS

The aim of the study was to assess the percentage of patients who did not achieve management targets (MT) for low-density lipoprotein-cholesterol (LDL-C), high-density lipoprotein-cholesterol (HDL-C) and triglycerides (TG), and explore factors related to failure to achieve lipid MT in Japanese patients with type 1 (T1D) and type 2 diabetes (T2D).

METHODS

This cross-sectional study included 795 patients (35% men) with T1D and 4018 patients (60% men) with T2D attending our diabetes center. MTs for serum lipids were in accordance with the guidelines of the Japan Atherosclerosis Society. Logistic regression analysis was performed to identify factors related to failure to achieve MTs for serum lipids.

RESULTS

The percentages of men/women who did not achieve MT for LDL-C were 34.1/31.8% in T1D and 40.5/52.7% in T2D. The corresponding values for TG were 35.1/14.0% in T1D and 50.1/47.9% in T2D, and for HDL-C were 2.5/0% in T1D and 8.6/2.9% in T2D. Increase in body mass index (BMI) and glycated hemoglobin (HbA1c) were significantly and independently associated with failure to achieve lipid MT in patients with T1D and T2D for both sexes.

CONCLUSIONS

The percentages of our patients who did not achieve serum lipid MT were relatively high in T1D and T2D, and higher HbA1c and BMI were associated with failure to achieve serum lipid MTs. More attention should be paid to lipid management in patients with diabetes especially who have higher HbA1c and BMI in our facility.

Use of a Metabolic Syndrome Severity Z Score to Track Risk During Treatment of Prediabetes: An Analysis of the Diabetes Prevention Program

OBJECTIVE

We assessed whether changes in metabolic syndrome (MetS) severity during the treatment of prediabetes are associated with reduced risk of type 2 diabetes mellitus (T2DM) and cardiovascular disease (CVD).

RESEARCH DESIGN AND METHODS

We analyzed data from the Diabetes Prevention Program (DPP) for 2,476 adults in 1996-1999 with prediabetes randomized to receive treatment with lifestyle modification, metformin, or placebo for 2-3 years and followed through 2014 for T2DM and CVD outcomes. We calculated effect sizes from baseline in a MetS severity z score (MetS-Z) and the individual MetS components, and assessed relationships between 1-year effect size and incident T2DM and CVD using hazard ratios (HRs) and mediation analysis.

RESULTS

Baseline MetS-Z and its components were associated with risk of incident T2DM and CVD. During year 1 of intervention, MetS-Z and its components decreased most with lifestyle modification, followed by treatment with metformin and placebo. Risk of T2DM within 1-5 years was most strongly associated with 1-year changes in MetS-Z and waist circumference (both HRs for a 1 SD increase = 1.80), whereas the risk of CVD was associated with a 1-year change in MetS-Z, glucose, and systolic blood pressure. In mediation analyses, the effect of lifestyle modification on T2DM risk was mediated by 1-year changes in MetS-Z, waist circumference, glucose, and triglycerides, whereas the effect of metformin was mediated by MetS-Z and glucose.

CONCLUSIONS

Changes in these risk indicators of MetS severity during intervention in the DPP reflect altered disease risk and may help in tracking earlier responses to treatment and in motivating patients.

Ten-year risk prediction models of complications and mortality of Chinese patients with diabetes mellitus in primary care in Hong Kong: a study protocol

INTRODUCTION

Diabetes mellitus (DM) is a major disease burden worldwide because it is associated with disabling and lethal complications. DM complication risk assessment and stratification is key to cost-effective management and tertiary prevention for patients with diabetes in primary care. Existing risk prediction functions were found to be inaccurate in Chinese patients with diabetes in primary care. This study aims to develop 10-year risk prediction models for total cardiovascular diseases (CVD) and all-cause mortality among Chinese patients with DM in primary care.

METHODS AND ANALYSIS

A 10-year cohort study on a population-based primary care cohort of Chinese patients with diabetes, who were receiving care in the Hospital Authority General Outpatient Clinic on or before 1 January 2008, were identified from the clinical management system database of the Hospital Authority. All patients with complete baseline risk factors will be included and followed from 1 January 2008 to 31 December 2017 for the development and validation of prediction models. The analyses will be carried out separately for men and women. Two-thirds of subjects will be randomly selected as the training sample for model development. Cox regressions will be used to develop 10-year risk prediction models of total CVD and all-cause mortality. The validity of models will be tested on the remaining one-third of subjects by Harrell's C-statistics and calibration plot. Risk prediction models for diabetic complications specific to Chinese patients in primary care will enable accurate risk stratification, prioritisation of resources and more cost-effective interventions for patients with DM in primary care.

ETHICS AND DISSEMINATION

The study was approved by the Institutional Review Board of the University of Hong Kong-the Hospital Authority Hong Kong West Cluster (reference number: UW 15-258).

TRIAL REGISTRATION NUMBER

NCT03299010; Pre-results.

Performance of Cardiovascular Disease Risk Scores in People Diagnosed With Type 2 Diabetes: External Validation Using Data From the National Scottish Diabetes Register

OBJECTIVE

To evaluate the performance of five cardiovascular disease (CVD) risk scores developed in diabetes populations and compare their performance to QRISK2.

RESEARCH DESIGN AND METHODS

A cohort of people diagnosed with type 2 diabetes between 2004 and 2016 was identified from the Scottish national diabetes register. CVD events were identified using linked hospital and death records. Five-year risk of CVD was estimated using each of QRISK2, ADVANCE (Action in Diabetes and Vascular disease: preterAx and diamicroN-MR Controlled Evaluation), Cardiovascular Health Study (CHS), New Zealand Diabetes Cohort Study (NZ DCS), Fremantle Diabetes Study, and Swedish National Diabetes Register (NDR) risk scores. Discrimination and calibration were assessed using the Harrell C statistic and calibration plots, respectively.

RESULTS

The external validation cohort consisted of 181,399 people with type 2 diabetes and no history of CVD. There were 14,081 incident CVD events within 5 years of follow-up. The 5-year observed risk of CVD was 9.7% (95% CI 9.6, 9.9). C statistics varied between 0.66 and 0.67 for all risk scores. QRISK2 overestimated risk, classifying 87% to be at high risk for developing CVD within 5 years; ADVANCE underestimated risk, and the Swedish NDR risk score calibrated well to observed risk.

CONCLUSIONS

None of the risk scores performed well among people with newly diagnosed type 2 diabetes. Using these risk scores to predict 5-year CVD risk in this population may not be appropriate.

Biomarkers of cardiovascular disease: contributions to risk prediction in individuals with diabetes

Cardiovascular disease is a leading cause of death, especially in individuals with diabetes mellitus, whose risk of morbidity and mortality due to cardiovascular disease is markedly increased compared with the general population. There has been growing interest in the identification of biomarkers of cardiovascular disease in people with diabetes. The present review focuses on the current and potential contributions of these biomarkers to predicting cardiovascular risk in individuals with diabetes. At present, certain biomarkers and biomarker combinations can lead to modest improvements in the prediction of cardiovascular disease in diabetes beyond traditional cardiovascular risk factors. Emerging technologies may enable the discovery of novel biomarkers and generate new information about known biomarkers (such as new combinations of biomarkers), which could lead to significant improvements in cardiovascular disease risk prediction. A critical question, however, is whether improvements in risk prediction will affect processes of care and decision making in clinical practice, as this will be required to achieve the ultimate goal of improving clinical outcomes in diabetes.

A comparison of the effects of low- and high-dose atorvastatin on lipoprotein metabolism and inflammatory cytokines in type 2 diabetes: Results from the Protection Against Nephropathy in Diabetes with Atorvastatin (PANDA) randomized trial

BACKGROUND

Statin therapy is recommended in type 2 diabetes (T2DM) although views on treatment intensity and therapeutic targets remain divided.

OBJECTIVES

Our objectives were to compare the effects of high-intensity and moderate-intensity atorvastatin treatment on lipoprotein metabolism and inflammatory markers and how frequently treatment goals are met in high-risk T2DM patients.

METHODS

Patients with T2DM and albuminuria (urinary albumin:creatinine ratio >5 mg/mmol, total cholesterol <7 mmol/L, proteinuria <2 g/d, creatinine <200 μmol/L) were randomized to receive atorvastatin 10 mg (n = 59) or 80 mg (n = 60) daily. Baseline and 1-year follow-up data are reported.

RESULTS

Patients were at high cardiovascular disease risk (observed combined mortality and nonfatal cardiovascular disease annual event rate 4.8%). The non-high-density lipoprotein cholesterol (HDL-C) goal of <2.6 mmol/L was achieved in 72% of participants receiving high-dose atorvastatin, but only in 40% on low-dose atorvastatin (P < .005). The proportion achieving apolipoprotein B (apoB) <0.8 g/L on high-dose and low-dose atorvastatin was 82% and 70%, respectively (NS). Total cholesterol, triglycerides, low-density lipoprotein (LDL) cholesterol, non-HDL-C, oxidized LDL, apoB, glyc-apoB, apolipoprotein E, and lipoprotein-associated phospholipase A2 decreased significantly, more so in participants on high-dose atorvastatin. Adiponectin increased and serum amyloid A decreased without dose dependency. Neither dose produced significant changes in HDL-C, cholesterol efflux, high-sensitivity C-reactive protein, glycated hemoglobin, serum paraoxonase-1, lecithin:cholesterol acyltransferase, or cholesteryl ester transfer protein.

CONCLUSIONS

High-dose atorvastatin is more effective in achieving non-HDL-C therapeutic goals and in modifying LDL-related parameters. Recommended apoB treatment targets may require revision. Despite the increase in adiponectin and the decrease in serum amyloid A, HDL showed no change in functionality.

Comparison of Machine Learning Approaches Toward Assessing the Risk of Developing Cardiovascular Disease as a Long-Term Diabetes Complication

The estimation of long-term diabetes complications risk is essential in the process of medical decision making. Guidelines for the management of Type 2 Diabetes Mellitus (T2DM) advocate calculating the Cardiovascular Disease (CVD) risk to initiate appropriate treatment. The objective of this study is to investigate the use of sophisticated machine learning techniques toward the development of personalized models able to predict the risk of fatal or nonfatal CVD incidence in T2DM patients. The important challenge of handling the unbalanced nature of the available dataset is addressed by applying novel ensemble strategies. Hybrid Wavelet Neural Networks (HWNNs) and Self-Organizing Maps (SOMs) constitute the primary models for building ensembles following a subsampling approach. Different methods for combining the decisions of the primary models are applied and comparatively assessed. Data from the 5-year follow up of 560 patients with T2DM are used for development and evaluation purposes. The highest discrimination performance (Area Under the Curve (AUC): 71.48%) is achieved by taking into account both the HWNN- and SOM- based primary models' outputs. The proposed method is superior to the Binomial Linear Regression (BLR) model justifying the need to apply more sophisticated techniques in order to produce reliable CVD risk scores.

Psychological interventions for diabetes-related distress in adults with type 2 diabetes mellitus

BACKGROUND

Many adults with type 2 diabetes mellitus (T2DM) experience a psychosocial burden and mental health problems associated with the disease. Diabetes-related distress (DRD) has distinct effects on self-care behaviours and disease control. Improving DRD in adults with T2DM could enhance psychological well-being, health-related quality of life, self-care abilities and disease control, also reducing depressive symptoms.

OBJECTIVES

To assess the effects of psychological interventions for diabetes-related distress in adults with T2DM.

SEARCH METHODS

We searched the Cochrane Library, MEDLINE, Embase, PsycINFO, CINAHL, BASE, WHO ICTRP Search Portal and ClinicalTrials.gov. The date of the last search was December 2014 for BASE and 21 September 2016 for all other databases.

SELECTION CRITERIA

We included randomised controlled trials (RCTs) on the effects of psychological interventions for DRD in adults (18 years and older) with T2DM. We included trials if they compared different psychological interventions or compared a psychological intervention with usual care. Primary outcomes were DRD, health-related quality of life (HRQoL) and adverse events. Secondary outcomes were self-efficacy, glycosylated haemoglobin A1c (HbA1c), blood pressure, diabetes-related complications, all-cause mortality and socioeconomic effects.

DATA COLLECTION AND ANALYSIS

Two review authors independently identified publications for inclusion and extracted data. We classified interventions according to their focus on emotion, cognition or emotion-cognition. We performed random-effects meta-analyses to compute overall estimates.

MAIN RESULTS

We identified 30 RCTs with 9177 participants. Sixteen trials were parallel two-arm RCTs, and seven were three-arm parallel trials. There were also seven cluster-randomised trials: two had four arms, and the remaining five had two arms. The median duration of the intervention was six months (range 1 week to 24 months), and the median follow-up period was 12 months (range 0 to 12 months). The trials included a wide spectrum of interventions and were both individual- and group-based.A meta-analysis of all psychological interventions combined versus usual care showed no firm effect on DRD (standardised mean difference (SMD) -0.07; 95% CI -0.16 to 0.03; P = 0.17; 3315 participants; 12 trials; low-quality evidence), HRQoL (SMD 0.01; 95% CI -0.09 to 0.11; P = 0.87; 1932 participants; 5 trials; low-quality evidence), all-cause mortality (11 per 1000 versus 11 per 1000; risk ratio (RR) 1.01; 95% CI 0.17 to 6.03; P = 0.99; 1376 participants; 3 trials; low-quality evidence) or adverse events (17 per 1000 versus 41 per 1000; RR 2.40; 95% CI 0.78 to 7.39; P = 0.13; 438 participants; 3 trials; low-quality evidence). We saw small beneficial effects on self-efficacy and HbA1c at medium-term follow-up (6 to 12 months): on self-efficacy the SMD was 0.15 (95% CI 0.00 to 0.30; P = 0.05; 2675 participants; 6 trials; low-quality evidence) in favour of psychological interventions; on HbA1c there was a mean difference (MD) of -0.14% (95% CI -0.27 to 0.00; P = 0.05; 3165 participants; 11 trials; low-quality evidence) in favour of psychological interventions. Our included trials did not report diabetes-related complications or socioeconomic effects.Many trials were small and were at high risk of bias for incomplete outcome data as well as possible performance and detection biases in the subjective questionnaire-based outcomes assessment, and some appeared to be at risk of selective reporting. There are four trials awaiting further classification. These are parallel RCTs with cognition-focused and emotion-cognition focused interventions. There are another 18 ongoing trials, likely focusing on emotion-cognition or cognition, assessing interventions such as diabetes self-management support, telephone-based cognitive behavioural therapy, stress management and a web application for problem solving in diabetes management. Most of these trials have a community setting and are based in the USA.

AUTHORS' CONCLUSIONS

Low-quality evidence showed that none of the psychological interventions would improve DRD more than usual care. Low-quality evidence is available for improved self-efficacy and HbA1c after psychological interventions. This means that we are uncertain about the effects of psychological interventions on these outcomes. However, psychological interventions probably have no substantial adverse events compared to usual care. More high-quality research with emotion-focused programmes, in non-US and non-European settings and in low- and middle-income countries, is needed.

Refining Long-Term Prediction of Cardiovascular Risk in Diabetes - The VILDIA Score

Cardiovascular risk assessment in patients with diabetes relies on traditional risk factors. However, numerous novel biomarkers have been found to be independent predictors of cardiovascular disease, which might significantly improve risk prediction in diabetic patients. We aimed to improve prediction of cardiovascular risk in diabetic patients by investigating 135 evolving biomarkers. Based on selected biomarkers a clinically applicable prediction algorithm for long-term cardiovascular mortality was designed. We prospectively enrolled 864 diabetic patients of the LUdwigshafen RIsk and Cardiovascular health (LURIC) study with a median follow-up of 9.6 years. Independent risk factors were selected using bootstrapping based on a Cox regression analysis. The following seven variables were selected for the final multivariate model: NT-proBNP, age, male sex, renin, diabetes duration, Lp-PLA2 and 25-OH vitamin D3. The risk score based on the aforementioned variables demonstrated an excellent discriminatory power for 10-year cardiovascular survival with a C-statistic of 0.76 (P < 0.001), which was significantly better than the established UKPDS risk engine (C-statistic = 0.64, P < 0.001). Net reclassification confirmed a significant improvement of individual risk prediction by 22% (95% confidence interval: 14–30%) compared to the UKPDS risk engine (P < 0.001). The VILDIA score based on traditional cardiovascular risk factors and reinforced with novel biomarkers outperforms previous risk algorithms.

Degree of Agreement between Cardiovascular Risk Stratification Tools

Background:

Cardiovascular disease (CVD) is the leading cause of morbidity and mortality in Brazil, and primary prevention care may be guided by risk stratification tools. The Framingham (FRS) and QRISK-2 (QRS) risk scores estimate 10-year overall cardiovascular risk in asymptomatic individuals, but the instrument of choice may lead to different therapeutic strategies.

Objective:

To evaluate the degree of agreement between FRS and QRS in 10-year overall cardiovascular risk stratification in disease-free individuals.

Methods:

Cross-sectional, observational, descriptive and analytical study in a convenience sample of 74 individuals attending the outpatient care service of a university hospital in Brazil between January 2014 and January 2015. After application of FRS and QRS, patients were classified in low/moderate risk (< 20%) or high risk (≥ 20%).

Results:

The proportion of individuals classified as at high risk was higher in FRS than in QRS (33.7% vs 21.6%). A synergic effect of male gender with systemic arterial hypertension was observed in both tools, and with for geriatric age group in QRS (p < 0.05) in high-risk stratum. The Kappa index was 0.519 (95%CI = 0.386-0.652; p < 0.001) between both instruments.

Conclusion:

There was a moderate agreement between FRS and QRS in estimating 10-year overall cardiovascular risk. The risk scores used in this study can identify synergism between variables, and their behavior is influenced by the population in which it was derived. It is important to recognize the need for calibrating risk scores for the Brazilian population.

Fundamento:

A doença cardiovascular (DCV) é a principal causa de morbimortalidade no Brasil, e a prevenção primária pode ser direcionada com ferramentas que estratificam o risco. Os escores de Framingham (ERF) e QRISK-2 (ERQ) estimam o risco cardiovascular (RCV) global em 10 anos em indivíduos assintomáticos, mas a escolha do instrumento pode implicar em terapêuticas distintas.

Objetivo:

Observar o grau de concordância entre o ERF e o ERQ, na estratificação do risco cardiovascular global em 10 anos, nos indivíduos livres da doença.

Métodos:

Estudo transversal, observacional, descritivo e analítico, com uma amostra de conveniência de 74 indivíduos, atendidos em um ambulatório de ensino de um hospital universitário brasileiro, no sul do país, de janeiro de 2014 a janeiro de 2015. O ERF e o ERQ foram aplicados nos pacientes, que foram classificados em baixo/moderado (< 20%) ou alto risco (≥ 20%).

Resultados:

A proporção de indivíduos classificados no estrato de alto risco foi superior no ERF que no ERQ (33,7% vs 21,6%), sendo identificado efeito sinérgico do gênero masculino com hipertensão arterial sistêmica nas duas ferramentas, e com faixa etária geriátrica no ERQ (p < 0,05) nesse estrato de risco. O índice de concordância Kappa entre os dois escores foi igual a 0,519 (IC95% = 0,386-0,652; p < 0,001).

Conclusão:

Houve concordância moderada entre o ERF e o ERQ, na estimativa de RCV global em 10 anos. Os escores utilizados podem identificar sinergismo entre as variáveis, e têm comportamento influenciado pela população na qual foram originados. É importante reconhecer a necessidade de escores calibrados para a população brasileira.

Pentraxin 3 and atherosclerosis among type 2 diabetic patients

Type 2 diabetes is contemporarily a major social and epidemiological problem and among others is a strong risk factor for cardiovascular diseases. Pentraxin 3, a potential early biomarker of atherosclerosis, is an acute-phase reactant produced by the peripheral tissues where the inflammation takes place. In this study we examined a group of patients with type 2 diabetes with and without cardiovascular complications compared to persons with normal glucose tolerance (patients with cardiovascular complications and healthy volunteers). Plasma pentraxin 3 concentration as well as some basic biochemical blood analysis were performed. Moreover, transcranial and carotid Doppler ultrasound examination as well as transthoracic echocardiography were performed. It turned out that there was an association of plasma pentraxin 3 concentration and carotid atherosclerosis found in the control group of patients with cardiovascular complications but with normal glucose tolerance. In the group of patients with type 2 diabetes and cardiovascular complications we have found an association of plasma pentraxin 3 concentration with diastolic left ventricular dysfunction. Additionally, in the group of patients with type 2 diabetes without cardiovascular disease plasma pentraxin 3 concentration was associated with elevated urinary albumin creatinine ratio. Further studies, on a larger group of patients, are required to confirm these observations.

Retinopathy Signs Improved Prediction and Reclassification of Cardiovascular Disease Risk in Diabetes: A prospective cohort study

CVD risk prediction in diabetics is imperfect, as risk models are derived mainly from the general population. We investigate whether the addition of retinopathy and retinal vascular caliber improve CVD prediction beyond established risk factors in persons with diabetes. We recruited participants from the Singapore Malay Eye Study (SiMES, 2004-2006) and Singapore Prospective Study Program (SP2, 2004-2007), diagnosed with diabetes but no known history of CVD at baseline. Retinopathy and retinal vascular (arteriolar and venular) caliber measurements were added to risk prediction models derived from Cox regression model that included established CVD risk factors and serum biomarkers in SiMES, and validated this internally and externally in SP2. We found that the addition of retinal parameters improved discrimination compared to the addition of biochemical markers of estimated glomerular filtration rate (eGFR) and high-sensitivity C-reactive protein (hsCRP). This was even better when the retinal parameters and biomarkers were used in combination (C statistic 0.721 to 0.774, p = 0.013), showing improved discrimination, and overall reclassification (NRI = 17.0%, p = 0.004). External validation was consistent (C-statistics from 0.763 to 0.813, p = 0.045; NRI = 19.11%, p = 0.036). Our findings show that in persons with diabetes, retinopathy and retinal microvascular parameters add significant incremental value in reclassifying CVD risk, beyond established risk factors.

Plasma Lipidomic Profiles Improve on Traditional Risk Factors for the Prediction of Cardiovascular Events in Type 2 Diabetes Mellitus

BACKGROUND

Clinical lipid measurements do not show the full complexity of the altered lipid metabolism associated with diabetes mellitus or cardiovascular disease. Lipidomics enables the assessment of hundreds of lipid species as potential markers for disease risk.

METHODS

Plasma lipid species (310) were measured by a targeted lipidomic analysis with liquid chromatography electrospray ionization-tandem mass spectrometry on a case-cohort (n=3779) subset from the ADVANCE trial (Action in Diabetes and Vascular Disease: Preterax and Diamicron-MR Controlled Evaluation). The case-cohort was 61% male with a mean age of 67 years. All participants had type 2 diabetes mellitus with ≥1 additional cardiovascular risk factors, and 35% had a history of macrovascular disease. Weighted Cox regression was used to identify lipid species associated with future cardiovascular events (nonfatal myocardial infarction, nonfatal stroke, and cardiovascular death) and cardiovascular death during a 5-year follow-up period. Multivariable models combining traditional risk factors with lipid species were optimized with the Akaike information criteria. C statistics and NRIs were calculated within a 5-fold cross-validation framework.

RESULTS

Sphingolipids, phospholipids (including lyso- and ether- species), cholesteryl esters, and glycerolipids were associated with future cardiovascular events and cardiovascular death. The addition of 7 lipid species to a base model (14 traditional risk factors and medications) to predict cardiovascular events increased the C statistic from 0.680 (95% confidence interval [CI], 0.678-0.682) to 0.700 (95% CI, 0.698-0.702; P<0.0001) with a corresponding continuous NRI of 0.227 (95% CI, 0.219-0.235). The prediction of cardiovascular death was improved with the incorporation of 4 lipid species into the base model, showing an increase in the C statistic from 0.740 (95% CI, 0.738-0.742) to 0.760 (95% CI, 0.757-0.762; P<0.0001) and a continuous net reclassification index of 0.328 (95% CI, 0.317-0.339). The results were validated in a subcohort with type 2 diabetes mellitus (n=511) from the LIPID trial (Long-Term Intervention With Pravastatin in Ischemic Disease).

CONCLUSIONS

The improvement in the prediction of cardiovascular events, above traditional risk factors, demonstrates the potential of plasma lipid species as biomarkers for cardiovascular risk stratification in diabetes mellitus.

CLINICAL TRIAL REGISTRATION

URL: https://clinicaltrials.gov. Unique identifier: NCT00145925.

Risk prediction of major complications in individuals with diabetes: the Atherosclerosis Risk in Communities Study

AIMS

To develop a prediction equation for 10-year risk of a combined endpoint (incident coronary heart disease, stroke, heart failure, chronic kidney disease, lower extremity hospitalizations) in people with diabetes, using demographic and clinical information, and a panel of traditional and non-traditional biomarkers.

METHODS

We included in the study 654 participants in the Atherosclerosis Risk in Communities (ARIC) study, a prospective cohort study, with diagnosed diabetes (visit 2; 1990-1992). Models included self-reported variables (Model 1), clinical measurements (Model 2), and glycated haemoglobin (Model 3). Model 4 tested the addition of 12 blood-based biomarkers. We compared models using prediction and discrimination statistics.

RESULTS

Successive stages of model development improved risk prediction. The C-statistics (95% confidence intervals) of models 1, 2, and 3 were 0.667 (0.64, 0.70), 0.683 (0.65, 0.71), and 0.694 (0.66, 0.72), respectively (p < 0.05 for differences). The addition of three traditional and non-traditional biomarkers [β-2 microglobulin, creatinine-based estimated glomerular filtration rate (eGFR), and cystatin C-based eGFR] to Model 3 significantly improved discrimination (C-statistic = 0.716; p = 0.003) and accuracy of 10-year risk prediction for major complications in people with diabetes (midpoint percentiles of lowest and highest deciles of predicted risk changed from 18-68% to 12-87%).

CONCLUSIONS

These biomarkers, particularly those of kidney filtration, may help distinguish between people at low versus high risk of long-term major complications.

Maximum BMI and microvascular complications in a cohort of Japanese patients with type 2 diabetes: the Japan Diabetes Complications Study

AIMS

The aim of this study was to examine the associations between possible indices of obesity based on information on weight history and the incidence of microvascular complications.

METHODS

A cohort of individuals with type 2 diabetes from 59 institutes in Japan was followed for 8years. Patients were classified into three categories according to weight at entrance and past maximum weight: normal (BMI at baseline <25kg/m(2) and maximum BMI <25kg/m(2)), past obesity (BMI at baseline <25kg/m(2) and maximum BMI ≥25kg/m(2)), and current obesity (BMI at baseline ≥25kg/m(2)) groups. The outcomes were diabetic retinopathy and overt nephropathy.

RESULTS

BMI at maximum and baseline of the 1809 patients was 26.5±3.5 and 23.1±3.0kg/m(2) (p<0.01), respectively (23.0±1.6 and 20.6±1.9kg/m(2) for normal, 27.4±2.0 and 22.8±1.4kg/m(2) for past obesity, and 30.1±2.9 and 27.0±1.8kg/m(2) for current obesity). The hazard ratios of past and current obesity compared to normal were 1.92 (95% CI, 1.08-3.41; p=0.03) and 2.21 (1.16-4.22; p=0.02), respectively, for overt nephropathy and 1.38 (1.05-1.83; p=0.02) and 1.64 (1.18-2.28; p<0.01), respectively, for diabetic retinopathy after adjustment for confounders.

CONCLUSIONS

Past obesity as well as current obesity were associated with increased risks of microvascular complications. Further identification of high-risk populations may be possible by classifying normal weight patients by past obesity.