How much might cardiovascular disease risk be reduced by intensive therapy in people with screen‐detected diabetes?

Aims To assess the cardiovascular disease (CVD) risk of people with screen‐detected Type 2 diabetes and to estimate the risk reduction achievable through early intensive pharmacological intervention. Methods In ADDITION‐Cambridge, diabetic patients were identified among people aged 40–69 years through a stepwise screening procedure including a risk score, random and fasting capillary blood glucose, HbA_1c and oral glucose tolerance test. In those without prior macrovascular disease, 10‐year CVD risk was computed using UK Prospective Diabetes Study (UKPDS) and Framingham engines. The absolute risk reduction achievable and its plausible range were predicted using relative risk reductions for individual therapies from published trials and sensitivity analysis. Results Of the 867 individuals with undiagnosed diabetes, 19% had pre‐existing CVD, 97% were overweight or obese, 86% had hypertension, 75% had dyslipidaemia, 20% had microalbuminuria and 18% were smokers. Of those with hypertension, 35% were not prescribed drugs and 42% were suboptimally treated. Of participants with dyslipidaemia, 68% were not prescribed medications and 22% were poorly controlled. Median 10‐year CVD risk was 34.0%[interquartile range (IQR) 26.2–44.6] in men and 21.5% (IQR 15.7–28.7) in women using the UKPDS engine; 38.6% (IQR 27.8–53.0) in men and 24.6% (IQR 17.2–32.9) in women using Framingham equations. In the most conservative scenario (no additive effect of therapies), the absolute risk reduction achievable through multifactorial therapy ranged from 4.9 to 9.5% (UKPDS) and from 5.4 to 10.5% (Framingham). The corresponding ranges of numbers needed to treat were 11–20 and 10–19. Conclusions People with screen‐detected diabetes have an adverse cardiovascular risk profile, which is potentially modifiable through application of existing treatment recommendations.     

Prognostic value of the Framingham cardiovascular risk equation and the UKPDS risk engine for coronary heart disease in newly diagnosed Type 2 diabetes: results from a United Kingdom study.

AIMS: To determine the prognostic value of the Framingham equation and the United Kingdom Prospective Diabetes Study (UKPDS) risk engine in patients with newly diagnosed Type 2 diabetes. METHODS: A community-based cohort (n=428; aged 30-74 years) free of clinically evident CVD and newly diagnosed with Type 2 diabetes were studied over a median 4.2 (sd+/-0.62) years. Predicted (using baseline variables at diagnosis) and observed proportions of primary CVD and CHD events were compared using the Framingham equations and the UKPDS risk engine (only CHD events). The discrimination (c-statistic) and calibration (HLchi2) of the risk equations were calculated. The sensitivity and specificity of the Framingham equation at a 15%, 10-year CHD risk threshold (NICE guidelines) was compared with that of the ADA lipid threshold (LDLc>or=2.6 mmol/l or triglycerides>or=4.5 mmol/l). RESULTS: The Framingham equations underestimated the overall number of cardiovascular events by 33% and coronary events by 32% and showed modest discrimination and poor calibration for CVD [c=0.673; HLchi2=32.8 (P<0.001)] and CHD risk [c=0.657; HLchi2=19.8 (P=0.011)]. Although the overall underestimate was lower and non-significant with the UKPDS risk engine for CHD (13%), its performance in terms of discrimination and calibration were similar [c=0.670; HLchi2=17.1 (P=0.029)]. The 15%, 10-year CHD risk threshold with both the Framingham and UKPDS risk engines had similar sensitivity for primary CVD as the lipid level threshold [85.7 and 89.8% vs. 93.9% (P=0.21 and 0.34)] and both had greater specificity [33.0 and 30.3% vs. 12.1% (P<0.001 and P<0.001)]. CONCLUSIONS: In people with newly diagnosed Type 2 diabetes, both the Framingham equation and UKPDS risk engine are moderately effective at identifying those at high-risk (discrimination) and are poor at quantifying risk (calibration). Nonetheless, at a population level, a 15% 10-year CHD risk threshold using either risk calculator has similar sensitivity as an approach based on a single lipid risk factor level and may have benefits in terms of cost-effectiveness given the improved specificity.     

Skin autofluorescence provides additional information to the UK Prospective Diabetes Study (UKPDS) risk score for the estimation of cardiovascular prognosis in type 2 diabetes mellitus

Aims/hypothesis  The UK Prospective Diabetes Study (UKPDS) risk engine has become a standard for cardiovascular risk assessment in type 2 diabetes mellitus. Skin autofluorescence was recently introduced as an alternative tool for cardiovascular risk assessment in diabetes. We investigated the prognostic value of skin autofluorescence for cardiovascular events in combination with the UKPDS risk engine in a cohort of patients with type 2 diabetes managed in primary care. Methods  Clinical, UKPDS risk engine and skin autofluorescence data were obtained at baseline in 2001–2002 in the type 2 diabetes group (n = 973). Follow-up data concerning fatal and non-fatal cardiovascular events (primary endpoint) were obtained till 2005. Patients were classified as ‘low risk’ when their 10 year UKPDS risk score for fatal cardiovascular events was <10%, and ‘high risk’ if >10%. Skin autofluorescence was measured non-invasively with an autofluorescence reader. Skin autofluorescence was classified by the median (i.e. low risk < median, high risk > median). Results  The incidence of cardiovascular events was 119 (44 fatal, 75 non-fatal). In multivariate analysis, skin autofluorescence, age, sex and diabetes duration were predictors for the primary endpoint. Addition of skin autofluorescence information to that from the UKPDS risk engine resulted in re-classification of 55 of 203 patients from the low-risk to the high-risk group. The 10 year cardiovascular event rate was higher in patients with a UKPDS score >10% when skin autofluorescence was above the median (55.8% vs 38.9%). Conclusions/interpretation  Skin autofluorescence provides additional information to the UKPDS risk engine which can result in risk re-classification of a substantial number of patients. It furthermore identifies patients who have a particularly high risk for developing cardiovascular events.     

Coronary risk assessment in subjects with type 2 diabetes mellitus. General population-based scores or specific scores?

Coronary risk in patients with type 2 diabetes mellitus can be calculated using population-based scores or diabetes-specific scores. Our objective was to compare the results with both scores in a group of patients with type 2 diabetes and no history of cardiovascular disease. We analyzed the results for 101 patients aged 40 to 65 years with type 2 diabetes and no prior cardiovascular disease. Two scales were used, one based on the general population (Framingham function adapted from the REGICOR study), and the other based on the population with type 2 diabetes mellitus (UKPDS risk engine). The average 10-year likelihood of coronary events was 5.8 (2.5)% and 15.7 (8.4)% for the REGICOR risk score and the UKPDS risk score, respectively (P<.001), with a Pearson correlation coefficient of 0.525 (P<.01). Risk was higher in men (19.2 [8.7]% based on the UKPDS score, and 5.6 [2.8]% based on the REGICOR score, P<.001). The figures for women were 11.3 [5.9]% and 5.9 [2.1]% with the UKPDS and REGICOR scores, respectively (P<.001). Our results suggest that substantially different findings are obtained when general population-based scores or specific scores are used to assess cardiovascular risk in subjects with type 2 diabetes.     

Associations of HbA1c and educational level with risk of cardiovascular events in 32 871 drug‐treated patients with Type 2 diabetes: a cohort study in primary care

Aims

To explore the association of HbA_1c and educational level with risk of cardiovascular events and mortality in patients with Type 2 diabetes.

Methods

A cohort of 32 871 patients with Type 2 diabetes aged 35 years and older identified by extracting data from electronic patient records for all patients who had a diagnosis of Type 2 diabetes and had glucose‐lowering agents prescribed between 1999 and 2009 at 84 primary care centres in Sweden. Associations of mean HbA_1c levels and educational level with risks of cardiovascular events and all‐cause mortality were analysed.

Results

The associations of HbA_1c with risk of all‐cause and cardiovascular mortality were J‐shaped, with the lowest risk observed for cardiovascular mortality at an HbA_1c level of 51 mmol/mol (6.8%) for subjects on oral agents and 56 mmol/mol (7.3%) in insulin‐treated patients. The lowest risk observed for all‐cause mortality was at an HbA_1c level of 51 mmol/mol (6.8%) for subjects on oral agents and 56 mmol/mol (7.3%) in insulin‐treated patients. There was an increased risk for cardiovascular death [hazard ratio 1.6 (1.2–2.1), P = 0.0008] at the lowest HbA_1c decile for subjects in the low education category. For subjects with higher education there was no evident J curve for cardiovascular death [hazard ratio 1.2 (0.8–1.6), P = 0.3873].

Conclusions

Our results lend support to the recent American Diabetes Association/ European Association for the Study of Diabetes position statement that emphasizes the importance of additional factors, including the propensity for hypoglycaemia, which should influence HbA_1c targets and treatment choices for individual patients. (Clinical Trials Registry No; NCT 01121315)     

The Framingham and UK Prospective Diabetes Study (UKPDS) risk equations do not reliably estimate the probability of cardiovascular events in a large ethnically diverse sample of patients with diabetes: the Action in Diabetes and Vascular Disease: Preterax and Diamicron-MR Controlled Evaluation (ADVANCE) Study

Aims/hypothesis

Available multivariable equations for cardiovascular risk assessment in people with diabetes have been derived either from the general population or from populations with diabetes. Their utility and comparative performance in a contemporary group of patients with type 2 diabetes are not well established. The aim of this study was to evaluate the performance of the Framingham and UK Prospective Diabetes Study (UKPDS) risk equations in participants who took part in the Action in Diabetes and Vascular disease: Preterax and Diamicron-MR Controlled Evaluation (ADVANCE) trial.

Methods

The 4-year risks of cardiovascular disease (CVD) and its constituents were estimated using two published Framingham and the UKPDS risk equations in 7,502 individuals with type 2 diabetes without prior known CVD at their enrolment in the trial.

Results

The risk of major CVD was overestimated by 170% (95% CI 146–195%) and 202% (176–231%) using the two Framingham equations. The risk of major coronary heart disease was overestimated by 198% (162–238%) with the UKPDS, and by 146% (117–179%) and 289% (243–341%) with the two different Framingham equations, respectively. The risks of stroke events were also overestimated with the UKPDS and one of the Framingham equations. The ability of these equations to rank risk among ADVANCE participants was modest, with c-statistics ranging from 0.57 to 0.71. Results stratified by sex, treatment allocation and ethnicity were broadly similar.

Conclusions/interpretation

Application of the Framingham and UKPDS risk equations to a contemporary treated group of patients with established type 2 diabetes is likely to substantially overestimate cardiovascular risk.     

Impact of using a non-diabetes-specific risk calculator on eligibility for statin therapy in type 2 diabetes

Aims/hypothesis  The aim of this study was to investigate the impact of using a non-diabetes-specific cardiovascular disease (CVD) risk calculator to determine eligibility for statin therapy according to current UK National Institute for Health and Clinical Excellence (NICE) guidelines for those patients with type 2 diabetes who are at an increased risk of CVD (10 year risk ≥20%). Methods  The 10 year CVD risks were estimated using the UK Prospective Diabetes Study (UKPDS) Risk Engine and the Framingham equation for 4,025 patients enrolled in the Lipids in Diabetes Study who had established type 2 diabetes and LDL-cholesterol <4.1 mmol/l. Results  The mean (SD) age of the patients was 60.7 (8.6) years, blood pressure 141/83 (17/10) mmHg and the total cholesterol:HDL-cholesterol ratio was 3.9 (1.0). The median (interquartile range) diabetes duration was 6 (3–11) years and the HbA_1c level was 8.0% (7.2–9.0%). The cohort comprised 65% men, 91% whites, 4% Afro-Caribbeans, 5% Asian Indians and 15% current smokers. More patients were classified as being at high risk by the UKPDS Risk Engine (65%) than by the Framingham CVD equation (63%) (p < 0.0001). The Framingham CVD equation classified fewer men and people aged <50 years old as high risk (p < 0.0001). There was no difference between the UKPDS Risk Engine and Framingham classification of women at high risk (p = 0.834). Conclusions/interpretation  These results suggest that the use of Framingham-derived rather than UKPDS Risk Engine-derived CVD risk estimates would deny about one in 25 patients statin therapy when applying current NICE guidelines. Thus, under these guidelines the choice of CVD risk calculator is important when assessing CVD risk in patients with type 2 diabetes, particularly for the identification of the relatively small proportion of younger people who require statin therapy.     

Effects of grape seed extract in Type 2 diabetic subjects at high cardiovascular risk: a double blind randomized placebo controlled trial examining metabolic markers,vascular tone,inflammation, oxidative stress and insulin sensitivity

Objective Current research has focused upon the potential links between novel markers of vascular risk such as endothelial dysfunction, oxidative stress, inflammation and insulin resistance in the pathogenesis of Type 2 diabetes and its complications. Grape seed extract (GSE), a flavonoid‐rich product, is a potential moderator of these markers. This study aimed to test the hypothesis that GSE may improve these markers in high‐risk cardiovascular subjects with Type 2 diabetes. Research design and methods Thirty‐two Type 2 diabetes mellitus patients, prescribed diet or oral glucose‐lowering agents, received GSE (600 mg/day) or placebo for 4 weeks in a double‐blinded randomized crossover trial. Markers of endothelial function (measured by photoplethysmography), oxidative stress [total antioxidant status (TAOS), reduced glutathione (GSH)/oxidized glutathione (GSSG)], inflammation [highly sensitive C‐reactive protein (hsCRP), urinary albumin : creatinine ratio), insulin resistance [homeostasis model assessment–insulin resistance (HOMA–IR)] and metabolism (fructosamine, lipid profile) was measured at baseline and after intervention with GSE or placebo. Results Baseline characteristics (16 male and 16 female): age 61.8 ± 6.36 years; body mass index 30.2 ± 5.92 kg/m²; diabetes duration 5.9 ± 2.14 years. Following GSE (but not placebo), significant changes were noted in fructosamine (282 ± 40.9 vs. 273 ± 50.2 mmol/l; P = 0.0004); whole blood GSH (2359 ± 823 vs. 3595 ± 1051 mmol/l; P < 0.01) and hsCRP (3.2 ± 3.65 vs. 2.0 ± 2.2 mg/l; P = 0.0006). Total cholesterol concentration also decreased (4.5 ± 0.96 vs. 4.3 ± 0.99 mmol/l; P = 0.05). No statistically significant changes were shown in endothelial function, HOMA–IR or TAOS. Conclusion GSE significantly improved markers of inflammation and glycaemia and a sole marker of oxidative stress in obese Type 2 diabetic subjects at high risk of cardiovascular events over a 4‐week period, which suggests it may have a therapeutic role in decreasing cardiovascular risk.     

Increased plasma levels of retinol‐binding protein 4 with visceral obesity is associated with cardiovascular risk factors

Aims/Introduction: Visceral obesity has been suggested to be an independent risk factor for cardiovascular disease (CVD); the role of adipokines in the risk for CVD is less clear. Aim of this study was to investigate the relationship between parameters of visceral obesity and index of CVD risk factors. Materials and Methods: A cross‐sectional analysis of healthy males (n = 116) and females (n = 175) for evaluation of clinical, laboratory and anthropometric parameters were undertaken. Abdominal subcutaneous (SAT) and visceral adipose tissues (VAT) were measured by computed tomography. Adipokines, including retinol‐binding protein 4 (RBP4) and adiponectin, were determined. The risk for CVD was estimated using the 10‐year Framingham Coronary Heart Disease Risk Point scale (Framingham score). Results: The Framingham score was increased in subjects with metabolic syndrome, and significantly increased with various indices of obesity, traditional risk factors of CVD, C‐reactive protein (CRP) and RBP4, but decreased with adiponectin. With multiple linear regression analysis, the Framingham score independently associated with age, smoking status, body mass index, triglyceride and RBP4. The magnitude of the Framingham score showed a linear trend of increase with CRP, VAT and RBP4 (all P < 0.001), but of decrease with SAT and adiponectin (all P < 0.05) at stratified levels of obesity. Conclusions: RBP4 is increased with visceral fat accumulation and associated with CVD risk factors independent of obesity or traditional risk factors. (J Diabetes Invest, doi: 10.1111/j.2040‐1124.2012.00213.x , 2012)     

An Australian cardiovascular risk equation for type 2 diabetes: the Fremantle Diabetes Study

Background: There is no valid cardiovascular disease (CVD) risk prediction equation for Australians with diabetes. The aim of this study is to develop and validate a multivariate risk function for 5‐year cardiovascular risk prediction in Australian type 2 diabetes patients. Methods: The Fremantle Diabetes Study is a community‐based longitudinal observational study. A total of 1240 type 2 diabetic patients (95.8% of the baseline cohort) with all required risk factor data were followed from baseline (1993–1996) for 5 years or until they experienced a cardiovascular event or died, whichever came first. CVD during follow up was defined as hospitalization for/with myocardial infarction or stroke, and death from cardiac or cerebrovascular causes or sudden death. Validation of the algorithm was performed on an independent diabetic cohort from the Busselton Health Study. Results: During 5570 patient‐years of follow up, 185 (14.9%) had at least one CVD event and 175 (14.1%) died (57.7% from CVD). Variables in the final model comprised age, sex, prior CVD, ln(urinary albumin : creatinine ratio), lnHbA_1c, ln(high density lipoprotein‐cholesterol), Southern European ethnic background and Aboriginality. The mean 5‐year predicted risk of a CVD event was 15.5%. Applied to the Busselton cohort, discrimination of the model was good (AUC = 0.84, P < 0.001) as was the goodness‐of‐fit (Hosmer–Lemeshow Ĉ‐test, P= 0.85) and accuracy (mean squared error (95% confidence interval) = 0.09 (0–0.76)). The positive and negative predictive values for a 10% 5‐year CVD risk cut‐off were 23.4% and 97.7% respectively. Conclusion: This simple diabetes‐specific 5‐year CVD risk equation is the first validated, population‐based Australian model. It should have a role in diabetes management in primary and specialist care.     

Albuminuria predicts cardiovascular events independently of left ventricular mass in hypertension: a LIFE substudy

We wanted to investigate whether urine albumin/creatinine ratio (UACR) and left ventricular (LV) mass, both being associated with diabetes and increased blood pressure, predicted cardiovascular events in patients with hypertension independently. After 2 weeks of placebo treatment, clinical, laboratory and echocardiographic variables were assessed in 960 hypertensive patients from the LIFE Echo substudy with electrocardiographic LV hypertrophy. Morning urine albumin and creatinine were measured to calculate UACR. The patients were followed for 60+/-4 months and the composite end point (CEP) of cardiovascular (CV) death, nonfatal stroke or nonfatal myocardial infarction was recorded. The incidence of CEP increased with increasing LV mass (below the lower quartile of 194 g to above the upper quartile of 263 g) in patients with UACR below (6.7, 5.0, 9.1%) and above the median value of 1.406 mg/mmol (9.7, 17.0, 19.0%(***)). Also the incidence of CV death increased with LV mass in patients with UACR below (0, 1.4, 1.3%) and above 1.406 mg/mmol (2.2, 6.4, 8.0%(**)). The incidence of CEP was predicted by logUACR (hazard ratio (HR)=1.44(**) for every 10-fold increase in UACR) after adjustment for Framingham risk score (HR=1.05(***)), history of peripheral vascular disease (HR=2.3(*)) and cerebrovascular disease (HR=2.1(*)). LV mass did not enter the model. LogUACR predicted CV death (HR=2.4(**)) independently of LV mass (HR=1.01(*) per gram) after adjustment for Framingham risk score (HR=1.05(*)), history of diabetes mellitus (HR=2.4(*)) and cerebrovascular disease (HR=3.2(*)). (*)P<0.05, (**)P<0.01, (***)P<0.001. In conclusion, UACR predicted CEP and CV death independently of LV mass. CV death was predicted by UACR and LV mass in an additive manner after adjustment for Framingham risk score and history of CV disease.     

Additional value of carotid artery intima-media thickness in cardiovascular risk assessment by Framingham risk-score in Type 2 diabetes: a retrospective cohort study

Background: The intima–media thickness (IMT) of the carotid artery is highly correlated with cardiovascular events in Type 2 diabetes mellitus (T2DM). The aim of the present study was to undertake a cardiovascular risk assessment in a group of patients (n = 102) who had been followed‐up for 10 years. Methods: Framingham risk score (FRS), IMT, and various other clinical parameters were evaluated retrospectively using Student’s t‐test, regression analysis, and χ² tests. Primary endpoints were defined as cardiovascular death, non‐fatal myocardial infarction, angina, and ischemic stroke. Results: The IMT (1.09 ± 0.32 vs 0.89 ± 0.25; P < 0.001) and percentage coronary risk as determined by the FRS (24.33 ± 11.07 vs 16.54 ± 8.35; P = 0.001) were significantly higher in patients presenting with any of the primary endpoints compared with patients in whom no cardiovascular morbidity or mortality was recorded. Other factors that significantly predicted cardiovascular mortality and morbidity included diastolic blood pressure and urinary albumin excretion (UAE; P < 0.001). The likelihood of primary endpoints could be predicted by UAE >30 mg/day, carotid artery IMT ≥0.9 mm, and FRS ≥20 (odds ratios 8.800, 3.377, and 2.807, respectively). Conclusion: Although FRS predicts 10‐year risk for cardiovascular mortality and morbidity in T2DM, we suggest that UAE and carotid artery IMT should also be considered in risk assessments.     

Incidence,prevalence and coronary heart disease risk level in known Type 2 diabetes: a sentinel practice network study in the Basque Country,Spain

Aims/hypothesis The aim of this study was to determine the incidence, prevalence and coronary heart disease risk in patients with known Type 2 (non-insulin-dependent) diabetes mellitus in a Basque Country sentinel practice network study.Methods During the year 2000 we did a survey among sentinel practitioners who registered information about previously and newly diagnosed Type 2 diabetic patients older than 24 years of age. We studied 65,651 people attending a primary care service in the Basque Country Health Service-Osakidetza. We collected information about diabetic complications and cardiovascular risk factors and measured the coronary heart disease risk in these patients.Results In the year 2000, the standardized cumulative incidence and prevalence of known Type 2 diabetes were 5.0 per 1000 (CI 95%: 4.9–5.1) and 4.6% (CI 95%: 4.5–4.7) respectively. Macroangiopathy was the most frequent complication both in the newly diagnosed (21.6%) and previously known Type 2 diabetic patients (33%). Total cholesterol 5.17 mmol/l and LDL cholesterol 2.58 mmol/l were found in 75% and 90% of newly diagnosed and 65% and 85% of previously diagnosed Type 2 diabetic patients respectively. Of the Type 2 diabetic patients 42% were obese and 80% had high blood pressure. More than 55% of the men compared with 44% of the women with Type 2 diabetes had high or very high risk of coronary heart disease (p<0.05).Conclusion/interpretation We report new epidemiological data on known Type 2 diabetes in the Basque Country. These patients have a high frequency of cardiovascular risk factors causing a high coronary heart disease risk.Abbreviations WHO MSVDD Word Health Organization Multinational Study of Vascular Disease in Diabetes - UKPDS United Kingdom Prospective Diabetes Study - MRFIT Multiple Risk Factor Intervention TrialThe authors wrote this article on behalf of and with the assistance of the Basque Country Sentinel Practice Surveillance Network: see acknowledgements for list of investigators     

Low HDL‐cholesterol is common in European Type 2 diabetic patients receiving treatment for dyslipidaemia: data from a pan‐European survey

Aims To measure the prevalence of low high‐density lipoprotein (HDL)‐cholesterol (men < 1.03 mmol/l; women < 1.29 mmol/l) in European Type 2 diabetic patients receiving treatment for dyslipidaemia. Methods The pan‐European Survey of HDL‐cholesterol measured lipids and other cardiovascular risk factors in 3866 patients with Type 2 diabetes and 4436 non‐diabetic patients undergoing treatment for dyslipidaemia in 11 European countries. Results Diabetic patients were more likely to be obese or hypertensive than non‐diabetic patients. Most patients received lifestyle interventions (87%) and/or a statin (89%); treatment patterns were similar between groups. Diabetic patients had [means (SD)] lower HDL‐cholesterol [1.22 (0.37) vs. 1.35 mmol/l (0.44) vs. non‐diabetic patients, P < 0.001] and higher triglycerides [2.32 (2.10) vs. 1.85 mmol/l (1.60), P < 0.001]. More diabetic vs. non‐diabetic patients had low HDL‐cholesterol (45% vs. 30%), high triglycerides (≥ 1.7 mmol/l; 57% vs. 42%) or both (32% vs. 19%). HDL‐cholesterol < 0.9 mmol/l was observed in 18% of diabetic and 12% of non‐diabetic subjects. Differences between diabetic and non‐diabetic groups were slightly greater for women. LDL‐ and total cholesterol were lower in the diabetic group [3.02 (1.05) vs. 3.30 mmol/l (1.14) and 5.12 (1.32) vs. 5.38 mmol/l (1.34), respectively, P < 0.001 for each]. Conclusions Low HDL‐cholesterol is common in diabetes: one in two diabetic women has low HDL‐cholesterol and one diabetic man in four has very low HDL‐cholesterol. Management strategies should include correction of low HDL‐cholesterol to optimize cardiovascular risk in diabetes.     

Effects of growth hormone replacement within the KIMS survey on estimated cardiovascular risk and predictors of risk reduction in patients with growth hormone deficiency

Objective There is growing evidence for an increased cardiovascular (CV) risk in untreated growth hormone deficiency of adults (GHD). We aimed at estimating CV risk with established algorithms before and during GH replacement in GHD and in healthy controls and at identifying predictors of risk reduction. Design A prospective, nested case‐control study. Patients We included 344 patients (44·7 ± 14·9 years) from the German Pfizer (formerly Kabi) International Metabolic Database (KIMS) cohort and included a healthy sex‐ and age‐matched control group from a primary care cohort. Measurements We calculated Framingham, Prospective Cardiovascular Münster Heart Study (PROCAM) and European Society of Cardiology (ESC) Score algorithms at all time points. In multivariate analyses, we analysed potential predictors of 2‐year reduction in CV risk, defined as a higher than median reduction in risk. Results In KIMS, the estimated 10‐year risks of CV events or CV mortality calculated with Framingham, PROCAM and ESC Score algorithms at baseline were 4·6%, 6·0% and 2·3%, respectively. These dropped to 2·4%, 4·8% and 0·8%, respectively, after 2 years of GH replacement (all P < 0·001 vs baseline) and returned to baseline levels after four years of GH replacement. In controls, the Framingham risk estimates were lower than in KIMS at baseline. All risk estimates increased during follow‐up and were significantly higher than in KIMS after four years (all P < 0·01). In backward‐selection models, high total cholesterol, low high‐density lipoprotein (HDL) cholesterol and male sex were significant predictors of response in most scores. Conclusion Two years of GH replacement decreased CV risk estimates approximately by half. Male sex, high total and low HDL cholesterol levels are potential predictors of good response.     

Emergency room visit: a red‐flag indicator for poor diabetes care

Aim To determine the association between emergency room (ER) admission and quality of diabetes care in the community. Methods In a nested case–control study of patients with Type 2 diabetes mellitus (DM) within a large health maintenance organization (HMO) in Israel, 919 patients who were admitted to one of West Jerusalem’s ERs between 1 May and 30 June 2004 were compared with 1952 control subjects not admitted. Data on study covariates were retrieved from the HMO’s computerized database and a subset of the study population was interviewed. Logistic regressions were conducted to estimate the odds ratios of being admitted according to different measures of quality of care, controlling for socio‐demographic variables, co‐morbidities and type of DM treatment. Results The main indices of quality of primary care that were inversely associated with visiting an ER during the study period included performance of a cholesterol test in the year prior to the index date [adjusted odds ratio (OR) 0.23, 95% confidence interval (CI) 0.19–0.29, P < 0.001], performance of glycated haemoglobin test (OR 0.26, 95% CI 0.24–0.29, P < 0.001), visiting an ophthalmologist (OR 0.47, 95% CI 0.32–0.68, P = 0.001), and recommendations to stop smoking (OR 0.10, 95% CI 0.05–0.21, P < 0.001). Conclusions Admission to the ER can be used as an indicator for poor quality of diabetes care. There is an association between ER admission and poor quality of diabetes care.     

The effectiveness and cost of single and multi-factorial cardiovascular risk factor modification to guideline targets in type 2 diabetes

AimsCardiovascular disease is the main cause of morbidity and mortality in type 2 diabetes (T2DM), at huge cost to the NHS. We investigated the potential effect on population cardiovascular risk and associated costs of single and multi-factorial intervention, to target levels, in individuals with T2DM.MethodsBaseline population means and proportions for cardiovascular risk factors were calculated for 159 patients with T2DM from 3 general practices. Predicted 10 year cardiovascular risk, and associated costs were calculated using the LIP2687 risk calculator, based on Framingham and UKPDS equations. Systolic blood pressure, HbA_1C, total cholesterol and HDL-cholesterol were altered to NICE and SIGN target levels and the model run again. The difference in outcomes was observed.Results45%, 76% and 38% of patients met NICE targets for cholesterol, systolic blood pressure and HbA1c, respectively. As expected, comparing the two guidelines, fewer patients met the ‘stricter’ targets (P = 0.0001). Treatment-to-target produced no significant difference in cardiovascular risk or costs, although greater reductions in outcomes were seen with multi-factorial intervention.ConclusionThis small study suggests that intervention in only those patients with the highest cardiovascular risk brings little reduction in population cardiovascular risk and associated health costs. Multi-factorial intervention in all patients with T2DM, regardless of baseline values, is likely to bring greater reductions. This raises the question as to whether the current emphasis on treatment to target should be modified to encourage multi-factorial intervention in all patients with T2DM, even those with baseline values below target levels.     

Effectiveness of a quality improvement intervention in reducing cardiovascular risk in hypertensive patients

INTRODUCTION AND OBJECTIVES: To evaluate the effect of a quality improvement intervention on the reduction of cardiovascular risk in patients with hypertension. PATIENTS AND METHOD: Quasi-experimental study involving two primary care centres. One centre was assigned to implement a quality improvement intervention (n = 482 patients), while at the other center "usual care" procedures were followed (control group, n = 360 patients). The quality improvement intervention consisted of a combined program designed for the medical staff and comprising audit, feedback, training sessions and implementation of clinical practice guidelines during 6 months. The main outcome measures were blood pressure, lipid levels, diabetes, smoking and cardiovascular risk. These values were compared before the intervention and after one year. RESULTS: The baseline characteristics of the patients were similar in both groups. Absolute cardiovascular risk decreased from 15.85% to 14.36% (P< .05) in the intervention group, and no significant change was observed in the control group (15.17% to 15.76%). The intervention led to a 2.07% decrease in cardiovascular risk (95%CI, 1.21-2.93; P< .05). The percentage of patients with high cardiovascular risk (> 20% at 10 years) decreased in the intervention group from 30% to 25%, and increased in the control group from 28% to 30%. Relative cardiovascular risk decreased from 2.03 to 1.75 (P< .05) in the intervention group, and from 1.98 to 1.92 (P> .05) in the control group. The intervention thus led to a 0.25 decrease in relative risk (95%CI: 0.14-0.35). CONCLUSIONS: Absolute and relative cardiovascular risk in patients with hypertension was reduced by a quality improvement intervention. The percentage of patients with high cardiovascular risk was also reduced.     

Comprehensive risk assessments of diabetic patients from seven Asian countries: The Joint Asia Diabetes Evaluation (JADE) program

Background: The aim of the web‐based Joint Asia Diabetes Evaluation (JADE) program is to establish a registry for quality assurance, monitoring, and evaluation. Methods: The JADE electronic portal provides templates for data collection, supplemented by risk stratification, care protocols, and decision support. Herein, data from 3687 patients with Type 2 diabetes, enrolled over 15 months in 2007–2009 from seven Asian countries, are reported. Results: Of the patients, 46.1% were men, the median (range) age was 58 (15–93 years), and median disease duration was 6.5 (0–71) years; 16.2% had at least one cardiovascular–renal complication (10.0% coronary heart disease, 3.3% stroke, 3.1% peripheral vascular disease, 0.4% end‐stage renal disease), 20.4% had diabetic retinopathy, 15.0% had sensory neuropathy, 7.5% had chronic kidney disease, and 20.7% of men had erectile dysfunction. Hypertension, dyslipidemia, and central obesity affected 84.6%, 76.8%, and 53.5% of patients, respectively. Treatment targets were HbA1c <7% in 35.3%, blood pressure <130/80 mmHg in 32.3%, and low‐density lipoprotein–cholesterol <2.6 mmol/L in 34.0%. The rate of attaining one, two, and three targets was 38.7%, 23.4%, and 5.4%, respectively. Using the JADE Risk Engine, 60% of patients with clinical complications and 20% of those with multiple risk parameters were predicted to have a major event within 5 years. Older age, short disease duration, adherence to diet, control of other risk factors, and not smoking were independently associated with HbA1c <7% (all P < 0.05). Conclusions: It is possible to use a web‐based protocol to establish a registry for risk stratification and facilitate early intervention.     

A retrospective observational study of people with Type 1 diabetes with self‐reported severe hypoglycaemia reveals high level of ambulance attendance but low levels of therapy change and specialist intervention

Aim

To evaluate the impact of severe hypoglycaemia on NHS resources and overall glycaemic control in adults with Type 1 diabetes.

Methods

An observational, retrospective study of adults (aged ≥ 18 years) with Type 1 diabetes reporting one or more episodes of severe hypoglycaemia during the preceding 24 months in 10 NHS hospital diabetes centres in England and Wales. The primary outcome was healthcare resource utilization associated with severe hypoglycaemia. Secondary outcomes included demographic and clinical characteristics, diabetes control and pathway of care.

Results

Some 140 episodes of severe hypoglycaemia were reported by 85 people during the 2‐year observation period. Ambulances were called in 99 of 140 (71%) episodes and Accident and Emergency attendance occurred in 26 of 140 (19%) episodes, whereas 29 of 140 (21%) episode required no immediate help from healthcare providers. Participants attended a median of 5 (range 0–58) diabetes clinic consultations during the observation period; 13% (70 of 552) of all consultations were severe hypoglycaemia‐related. Of the HbA_1c measurements recorded closest prior to severe hypoglycaemia (n = 119), only 7 of 119 measurements were < 48 mmol/mol (< 6.5%) and mean HbA_1c was 70 (sd 19) mmol/mol (8.5%, sd 1.7%). Some 119 changes to diabetes treatment were recorded during the observation period (median/person 0;, range 0–11), of which 52 of 119 changes (44%) followed severe hypoglycaemic events.

Conclusions

We observed a high level of ambulance service intervention but surprisingly low levels of hypoglycaemia follow‐up, therapy change and specialist intervention in people self‐reporting severe hypoglycaemia. These results suggest there may be important gaps in care pathways for people with Type 1 diabetes self‐reporting severe hypoglycaemia.