Predictive performance of a competing risk cardiovascular prediction tool CRISK compared to QRISK3 in older people and those with comorbidity: population cohort study.

BACKGROUND: Recommended cardiovascular disease (CVD) prediction tools do not account for competing mortality risk and over-predict incident CVD in older and multimorbid people. The aim of this study was to derive and validate a competing risk model (CRISK) to predict incident CVD and compare its performance to that of QRISK3 in UK primary care. METHODS: We used UK linked primary care data from the Clinical Practice Research Datalink (CPRD) GOLD to identify people aged 25-84 years with no previous CVD or statin treatment split into derivation and validation cohorts. In the derivation cohort, we derived models using the same covariates as QRISK3 with Fine-Gray competing risk modelling alone (CRISK) and with Charlson Comorbidity score (CRISK-CCI) as an additional predictor of non-CVD death. In a separate validation cohort, we examined discrimination and calibration compared to QRISK3. Reclassification analysis examined the number of patients recommended for treatment and the estimated number needed to treat (NNT) to prevent a new CVD event. RESULTS: The derivation and validation cohorts included 989,732 and 494,865 women and 946,784 and 473,392 men respectively. Overall discrimination of CRISK and CRISK-CCI were excellent and similar to QRISK3 (for women, C-statistic = 0.863/0.864/0.863 respectively; for men 0.833/0.819/0.832 respectively). CRISK and CRISK-CCI calibration overall and in younger people was excellent. CRISK over-predicted in older and multimorbid people although performed better than QRISK3, whilst CRISK-CCI performed the best. The proportion of people reclassified by CRISK-CCI varied by QRISK3 risk score category, with 0.7-9.7% of women and 2.8-25.2% of men reclassified as higher risk and 21.0-69.1% of women and 27.1-57.4% of men reclassified as lower risk. Overall, CRISK-CCI recommended fewer people for treatment and had a lower estimated NNT at 10% risk threshold. Patients reclassified as higher risk were younger, had lower SBP and higher BMI, and were more likely to smoke. CONCLUSIONS: CRISK and CRISK-CCI performed better than QRISK3. CRISK-CCI recommends fewer people for treatment and has a lower NNT to prevent a new CVD event compared to QRISK3. Competing risk models should be recommended for CVD primary prevention treatment recommendations.

Effect of atorvastatin on glycaemia progression in patients with diabetes: an analysis from the Collaborative Atorvastatin in Diabetes Trial (CARDS).

AIMS/HYPOTHESIS: In an individual-level analysis we examined the effect of atorvastatin on glycaemia progression in type 2 diabetes and whether glycaemia effects reduce the prevention of cardiovascular disease (CVD) with atorvastatin. METHODS: The study population comprised 2,739 people taking part in the Collaborative Atorvastatin Diabetes Study (CARDS) who were randomised to receive atorvastatin 10 mg or placebo and who had post-randomisation HbA1c data. This secondary analysis used Cox regression to estimate the effect of atorvastatin on glycaemia progression, defined as an increase in HbA1c of ≥ 0.5% (5.5 mmol/mol) or intensification of diabetes therapy. Mixed models were used to estimate the effect of atorvastatin on HbA1c as a continuous endpoint. RESULTS: Glycaemia progression occurred in 73.6% of participants allocated placebo and 78.1% of those allocated atorvastatin (HR 1.18 [95% CI 1.08, 1.29], p < 0.001) by the end of follow-up. The HR was 1.22 (95% CI 1.19, 1.35) in men and 1.11 (95% CI 0.95, 1.29) in women (p = 0.098 for the sex interaction). A similar effect was seen in on-treatment analyses: HR 1.20 (95% CI 1.07, 1.35), p = 0.001. The net mean treatment effect on HbA1c was 0.14% (95% CI 0.08, 0.21) (1.5 mmol/mol). The effect did not increase through time. Diabetes treatment intensification alone did not differ with statin allocation. Neither baseline nor 1-year-attained HbA1c predicted subsequent CVD, and the atorvastatin effect on CVD did not vary by HbA1c change (interaction p value 0.229). CONCLUSIONS/INTERPRETATION: The effect of atorvastatin 10 mg on glycaemia progression among those with diabetes is statistically significant but very small, is not significantly different between sexes, does not increase with duration of statin and does not have an impact on the magnitude of CVD risk reduction with atorvastatin.

Effect of atorvastatin on C-reactive protein and benefits for cardiovascular disease in patients with type 2 diabetes: analyses from the Collaborative Atorvastatin Diabetes Trial.

AIMS/HYPOTHESIS: We investigated whether atorvastatin 10 mg daily lowered C-reactive protein (CRP) and whether the effects of atorvastatin on cardiovascular disease (CVD) varied by achieved levels of CRP and LDL-cholesterol. METHODS: CRP levels were measured at baseline and 1 year after randomisation to atorvastatin in 2,322 patients with type 2 diabetes (40-75 years, 69% males) in a secondary analysis of the Collaborative Atorvastatin Diabetes Study, a randomised placebo-controlled trial. We used Cox regression models to test the effects on subsequent CVD events (n = 147) of CRP and LDL-cholesterol lowering at 1 year. RESULTS: After 1 year, the atorvastatin arm showed a net CRP lowering of 32% (95% CI -40%, -22%) compared with placebo. The CRP response was highly variable, with 45% of those on atorvastatin having no decrease in CRP (median [interquartile range, IQR] per cent change -9.8% [-57%, 115%]). The LDL-cholesterol response was less variable, with a median (IQR) within-person per cent change of -41% (-51%, -31%). Baseline CRP did not predict CVD over 3.8 years of follow-up (HRper SD log 0.89 [95% CI 0.75, 1.06]), whereas baseline LDL-cholesterol predicted CVD (HRper SD 1.21 [95% CI 1.02, 1.44]), as did on-treatment LDL-cholesterol. There was no significant difference in the reduction in CVD by atorvastatin, with above median (HR 0.57) or below median (HR 0.52) change in CRP or change in LDL-cholesterol (HR 0.61 vs 0.50). CONCLUSIONS/INTERPRETATION: CRP was not a strong predictor of CVD. Statin efficacy did not vary with achieved CRP despite considerable variability in CRP response. The use of CRP as an indicator of efficacy of statin therapy on CVD risk in patients with type 2 diabetes is not supported by these data. Trial registration NCT00327418.

Protein biomarkers for the prediction of cardiovascular disease in type 2 diabetes.

AIMS/HYPOTHESIS: We selected the most informative protein biomarkers for the prediction of incident cardiovascular disease (CVD) in people with type 2 diabetes. METHODS: In this nested case-control study we measured 42 candidate CVD biomarkers in 1,123 incident CVD cases and 1,187 controls with type 2 diabetes selected from five European centres. Combinations of biomarkers were selected using cross-validated logistic regression models. Model prediction was assessed using the area under the receiver operating characteristic curve (AUROC). RESULTS: Sixteen biomarkers showed univariate associations with incident CVD. The most predictive subset selected by forward selection methods contained six biomarkers: N-terminal pro-B-type natriuretic peptide (OR 1.69 per 1 SD, 95% CI 1.47, 1.95), high-sensitivity troponin T (OR 1.29, 95% CI 1.11, 1.51), IL-6 (OR 1.13, 95% CI 1.02, 1.25), IL-15 (OR 1.15, 95% CI 1.01, 1.31), apolipoprotein C-III (OR 0.79, 95% CI 0.70, 0.88) and soluble receptor for AGE (OR 0.84, 95% CI 0.76, 0.94). The prediction of CVD beyond clinical covariates improved from an AUROC of 0.66 to 0.72 (AUROC for Framingham Risk Score covariates 0.59). In addition to the biomarkers, the most important clinical covariates for improving prediction beyond the Framingham covariates were estimated GFR, insulin therapy and HbA1c. CONCLUSIONS/INTERPRETATION: We identified six protein biomarkers that in combination with clinical covariates improved the prediction of our model beyond the Framingham Score covariates. Biomarkers can contribute to improved prediction of CVD in diabetes but clinical data including measures of renal function and diabetes-specific factors not included in the Framingham Risk Score are also needed.

Estimated life expectancy in a Scottish cohort with type 1 diabetes, 2008-2010.

IMPORTANCE: Type 1 diabetes has historically been associated with a significant reduction in life expectancy. Major advances in treatment of type 1 diabetes have occurred in the past 3 decades. Contemporary estimates of the effect of type 1 diabetes on life expectancy are needed. OBJECTIVE: To examine current life expectancy in people with and without type 1 diabetes in Scotland. We also examined whether any loss of life expectancy in patients with type 1 diabetes is confined to those who develop kidney disease. DESIGN, SETTING, AND PARTICIPANTS: Prospective cohort of all individuals alive in Scotland with type 1 diabetes who were aged 20 years or older from 2008 through 2010 and were in a nationwide register (n=24,691 contributing 67,712 person-years and 1043 deaths). MAIN OUTCOMES AND MEASURES: Differences in life expectancy between those with and those without type 1 diabetes and the percentage of the difference due to various causes. RESULTS: Life expectancy at an attained age of 20 years was an additional 46.2 years among men with type 1 diabetes and 57.3 years among men without it, an estimated loss in life expectancy with diabetes of 11.1 years (95% CI, 10.1-12.1). Life expectancy from age 20 years was an additional 48.1 years among women with type 1 diabetes and 61.0 years among women without it, an estimated loss with diabetes of 12.9 years (95% CI, 11.7-14.1). Even among those with type 1 diabetes with an estimated glomerular filtration rate of 90 mL/min/1.73 m2 or higher, life expectancy was reduced (49.0 years in men, 53.1 years in women) giving an estimated loss from age 20 years of 8.3 years (95% CI, 6.5-10.1) for men and 7.9 years (95% CI, 5.5-10.3) for women. Overall, the largest percentage of the estimated loss in life expectancy was related to ischemic heart disease (36% in men, 31% in women) but death from diabetic coma or ketoacidosis was associated with the largest percentage of the estimated loss occurring before age 50 years (29.4% in men, 21.7% in women). CONCLUSIONS AND RELEVANCE: Estimated life expectancy for patients with type 1 diabetes in Scotland based on data from 2008 through 2010 indicated an estimated loss of life expectancy at age 20 years of approximately 11 years for men and 13 years for women compared with the general population without type 1 diabetes.

Derivation and validation of the CFracture competing risk fracture prediction tool compared with QFracture in older people and people with comorbidity: a population cohort study.

BACKGROUND: UK guidelines recommend the QFracture tool to predict the risk of major osteoporotic fracture and hip fracture, but QFracture calibration is poor, partly because it does not account for competing mortality risk. The aim of this study was to derive and validate a competing risk model to predict major osteoporotic fracture and hip fracture (CFracture) and compare its performance with that of QFracture in UK primary care. METHODS: We used UK linked primary care data from the Clinical Practice Research Datalink GOLD database to identify people aged 30-99 years, split into derivation and validation cohorts. In the derivation cohort, we derived models (CFracture) using the same covariates as QFracture with Fine-Gray competing risk modelling, and included the Charlson Comorbidity Index score as an additional predictor of non-fracture death. In a separate validation cohort, we examined discrimination (using Harrell's C-statistic) and calibration of CFracture compared with QFracture. Reclassification analysis examined differences in the characteristics of patients reclassified as higher risk by CFracture but not by QFracture. FINDINGS: The derivation cohort included 1 831 606 women and 1 789 820 men, and the validation cohort included 915 803 women and 894 910 men. Overall discrimination of CFracture was excellent (C-statistic=0·813 [95% CI 0·810-0·816] for major osteoporotic fracture and 0·914 [0·908-0·919] for hip fracture in women; 0·734 [0·729-0·740] for major osteoporotic fracture and 0·886 [0·877-0·895] for hip fracture in men) and was similar to QFracture. CFracture calibration overall and in people younger than 75 years was generally excellent. CFracture overpredicted major osteoporotic fracture and hip fracture in older people and people with comorbidity, but was better calibrated than QFracture. Patients classified as high-risk by CFracture but not by QFracture had a higher prevalence of current smoking and previous fracture, but lower prevalence of dementia, cancer, cardiovascular disease, renal disease, and diabetes. INTERPRETATION: CFracture has similar discrimination to QFracture but is better calibrated overall and in younger people. Both models performed poorly in adults aged 85 years and older. Competing risk models should be recommended for fracture risk prediction to guide treatment recommendations. FUNDING: National Institute for Health and Care Research, Wellcome Trust, Health Data Research UK.

Risk of cardiovascular disease and total mortality in adults with type 1 diabetes: Scottish registry linkage study.

BACKGROUND: Randomized controlled trials have shown the importance of tight glucose control in type 1 diabetes (T1DM), but few recent studies have evaluated the risk of cardiovascular disease (CVD) and all-cause mortality among adults with T1DM. We evaluated these risks in adults with T1DM compared with the non-diabetic population in a nationwide study from Scotland and examined control of CVD risk factors in those with T1DM. METHODS AND FINDINGS: The Scottish Care Information-Diabetes Collaboration database was used to identify all people registered with T1DM and aged ≥20 years in 2005-2007 and to provide risk factor data. Major CVD events and deaths were obtained from the national hospital admissions database and death register. The age-adjusted incidence rate ratio (IRR) for CVD and mortality in T1DM (n = 21,789) versus the non-diabetic population (3.96 million) was estimated using Poisson regression. The age-adjusted IRR for first CVD event associated with T1DM versus the non-diabetic population was higher in women (3.0: 95% CI 2.4-3.8, p<0.001) than men (2.3: 2.0-2.7, p<0.001) while the IRR for all-cause mortality associated with T1DM was comparable at 2.6 (2.2-3.0, p<0.001) in men and 2.7 (2.2-3.4, p<0.001) in women. Between 2005-2007, among individuals with T1DM, 34 of 123 deaths among 10,173 who were <40 years and 37 of 907 deaths among 12,739 who were ≥40 years had an underlying cause of death of coma or diabetic ketoacidosis. Among individuals 60-69 years, approximately three extra deaths per 100 per year occurred among men with T1DM (28.51/1,000 person years at risk), and two per 100 per year for women (17.99/1,000 person years at risk). 28% of those with T1DM were current smokers, 13% achieved target HbA(1c) of <7% and 37% had very poor (≥9%) glycaemic control. Among those aged ≥40, 37% had blood pressures above even conservative targets (≥140/90 mmHg) and 39% of those ≥40 years were not on a statin. Although many of these risk factors were comparable to those previously reported in other developed countries, CVD and mortality rates may not be generalizable to other countries. Limitations included lack of information on the specific insulin therapy used. CONCLUSIONS: Although the relative risks for CVD and total mortality associated with T1DM in this population have declined relative to earlier studies, T1DM continues to be associated with higher CVD and death rates than the non-diabetic population. Risk factor management should be improved to further reduce risk but better treatment approaches for achieving good glycaemic control are badly needed. Please see later in the article for the Editors' Summary.

Effect of competing mortality risks on predictive performance of the QFracture risk prediction tool for major osteoporotic fracture and hip fracture: external validation cohort study in a UK primary care population.

Objective: To externally evaluate the QFracture risk prediction tool for predicting the risk of major osteoporotic fracture and hip fracture. Design: External validation cohort study. Setting: UK primary care population. Linked general practice (Clinical Practice Research Datalink (CPRD) Gold), mortality registration (Office of National Statistics), and hospital inpatient (Hospital Episode Statistics) data, from 1 January 2004 to 31 March 2016. Participants: 2 747 409 women and 2 684 730 men, aged 30-99 years, with up-to-standard linked data that had passed CPRD checks for at least one year. Main outcome measures: Two outcomes were modelled based on the QFracture: major osteoporotic fracture and hip fracture. Major osteoporotic fracture was defined as any hip, distal forearm, proximal humerus, or vertebral crush fracture, from general practice, hospital discharge, and mortality data. The QFracture 10 year predicted risk of major osteoporotic fracture and hip fracture was calculated, and performance evaluated versus observed 10 year risk of fracture in the whole population, and in subgroups based on age and comorbidity. QFracture calibration was examined accounting for, and not accounting for, competing risk of mortality from causes other than the major osteoporotic fracture. Results: 2 747 409 women with 95 598 major osteoporotic fractures and 36 400 hip fractures, and 2 684 730 men with 34 321 major osteoporotic fractures and 13 379 hip fractures were included in the analysis. The incidence of all fractures was higher than in the QFracture internal derivation. Competing risk of mortality was more common than fracture from middle age onwards. QFracture discrimination in the whole population was excellent or good for major osteoporotic fracture and hip fracture (Harrell's C statistic in women 0.813 and 0.918, and 0.738 and 0.888 in men, respectively), but was poor to moderate in age subgroups (eg, Harrell's C statistic in women and men aged 85-99 years was 0.576 and 0.624 for major osteoporotic fractures, and 0.601 and 0.637 for hip fractures, respectively). Without accounting for competing risks, QFracture systematically under-predicted the risk of fracture in all models, and more so for major osteoporotic fracture than for hip fracture, and more so in older people. Accounting for competing risks, QFracture still under-predicted the risk of fracture in the whole population, but over-prediction was considerable in older age groups and in people with high comorbidities at high risk of fracture. Conclusions: The QFracture risk prediction tool systematically under-predicted the risk of fracture (because of incomplete determination of fracture rates) and over-predicted the risk in older people and in those with more comorbidities (because of competing mortality). The use of QFracture in its current form needs to be reviewed, particularly in people at high risk of death from other causes.

Targets of statin therapy: LDL cholesterol, non-HDL cholesterol, and apolipoprotein B in type 2 diabetes in the Collaborative Atorvastatin Diabetes Study (CARDS).

BACKGROUND: LDL can vary considerably in its cholesterol content; thus, lowering LDL cholesterol (LDLC) as a goal of statin treatment implies the existence of considerable variation in the extent to which statin treatment removes circulating LDL particles. This consideration is particularly applicable in diabetes mellitus, in which LDL is frequently depleted of cholesterol. METHODS: Type 2 diabetes patients randomly allocated to 10 mg/day atorvastatin (n = 1154) or to placebo (n = 1196) for 1 year were studied to compare spontaneous and statin-induced apolipoprotein B (apo B) concentrations (a measure of LDL particle concentration) at LDLC and non-HDL cholesterol (non-HDLC) concentrations proposed as statin targets in type 2 diabetes. RESULTS: Patients treated with atorvastatin produced lower serum apo B concentrations at any given LDLC concentration than patients on placebo. An LDLC concentration of 1.8 mmol/L (70 mg/dL) during atorvastatin treatment was equivalent to a non-HDLC concentration of 2.59 mmol/L (100 mg/dL) or an apo B concentration of 0.8 g/L. At the more conservative LDLC targets of 2.59 mmol/L (100 mg/dL) and 3.37 mmol/L (130 mg/dL) for non-HDLC, however, the apo B concentration exceeded the 0.9-g/L value anticipated in the recent Consensus Statement from the American Diabetes Association and the American College of Cardiology. CONCLUSIONS: The apo B concentration provides a more consistent goal for statin treatment than the LDLC or non-HDLC concentration.

Effects of atorvastatin on kidney outcomes and cardiovascular disease in patients with diabetes: an analysis from the Collaborative Atorvastatin Diabetes Study (CARDS).

BACKGROUND: We examined whether atorvastatin affects diabetic kidney disease and whether the effect of atorvastatin on cardiovascular disease (CVD) varies by kidney status in patients with diabetes. STUDY DESIGN: The Collaborative Atorvastatin Diabetes Study (CARDS) randomized placebo-controlled trial. SETTING & PARTICIPANTS: Patients with type 2 diabetes and no prior CVD (n = 2,838). INTERVENTION: Random allocation to atorvastatin, 10 mg/d, or placebo, with a median follow-up of 3.9 years. OUTCOMES: Estimated glomerular filtration rate (eGFR), albuminuria, CVD. MEASUREMENTS: Baseline and follow-up GFRs were estimated by using the Modification of Diet in Renal Disease Study equation. Urinary albumin-creatinine ratio was measured on spot urine samples. RESULTS: At baseline, 34% of patients had an eGFR of 30 to 60 mL/min/1.73 m(2). Atorvastatin treatment was associated with a modest improvement in annual change in eGFR (net, 0.18 mL/min/1.73 m(2)/y; 95% confidence interval [CI], 0.04 to 0.32; P = 0.01) that was most apparent in those with albuminuria (net improvement, 0.38 mL/min/1.73 m(2)/y; P = 0.03). At baseline, 21.5% of patients had albuminuria and an additional 6.8% developed albuminuria during follow-up. Atorvastatin did not influence the incidence of albuminuria (hazard ratio, 1.49; 95% CI, 0.73 to 3.04; P = 0.3) or regression to normoalbuminuria (hazard ratio, 1.19; 95% CI, 0.57 to 2.49; P = 0.6). In 970 patients with a moderately decreased eGFR of 30 to 60 mL/min/1.73 m(2), there was a 42% reduction in major CVD events with treatment, including a 61% reduction in stroke. This treatment effect was similar to the 37% (95% CI, 17 to 52; P < 0.001) reduction in CVD observed in the study overall (P = 0.4 for the eGFR-treatment interaction). LIMITATIONS: Low incidence rates of albuminuria and transition to more severe kidney status limit power to detect treatment effects. CONCLUSIONS: A modest beneficial effect of atorvastatin on eGFR, particularly in those with albuminuria, was observed. Atorvastatin did not influence albuminuria incidence. Atorvastatin was effective at decreasing CVD in those with and without a moderately decreased eGFR and achieved a high absolute benefit.

Analysis of efficacy and safety in patients aged 65-75 years at randomization: Collaborative Atorvastatin Diabetes Study (CARDS).

OBJECTIVE: Rates of cardiovascular disease are highest in the elderly. Lipid-lowering statin therapy reduces the proportional risk as effectively in older patients as in younger individuals; however, limited data are available for elderly patients with type 2 diabetes. We conducted a post hoc analysis to compare the efficacy and safety of atorvastatin among 1,129 patients aged 65-75 years at randomization with 1,709 younger patients in the Collaborative Atorvastatin Diabetes Study (CARDS). RESEARCH DESIGN AND METHODS: CARDS was a randomized placebo-controlled trial of 10 mg/day atorvastatin for primary prevention of cardiovascular disease in patients aged 40-75 years with LDL cholesterol concentrations

Development and Validation of a Model to Predict Absolute Vascular Risk Reduction by Moderate-Intensity Statin Therapy in Individual Patients With Type 2 Diabetes Mellitus: The Anglo Scandinavian Cardiac Outcomes Trial, Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial, and Collaborative Atorvastatin Diabetes Study.

BACKGROUND: In this study, we aimed to translate the average relative effect of statin therapy from trial data to the individual patient with type 2 diabetes mellitus by developing and validating a model to predict individualized absolute risk reductions (ARR) of cardiovascular events. METHODS AND RESULTS: Data of 2725 patients with type 2 diabetes mellitus from the Lipid Lowering Arm of the Anglo Scandinavian Cardiac Outcomes Trial (ASCOT-LLA) study (atorvastatin 10 mg versus placebo) were used for model derivation. The model was based on 8 clinical predictors including treatment allocation (statin/placebo). Ten-year individualized ARR on major cardiovascular events by statin therapy were calculated for each patient by subtracting the estimated on-treatment risk from the estimated off-treatment risk. Predicted 10-year ARR by statin therapy was <2% for 13% of the patients. About 30% had an ARR of >4% (median ARR, 3.2%; interquartile range, 2.5%-4.3%; 95% confidence interval for 3.2% ARR, -1.4% to 6.8%). Addition of treatment interactions did not improve model performance. Therefore, the wide distribution in ARR was a consequence of the underlying distribution in cardiovascular risk enrolled in these trials. External validation of the model was performed in data from the Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT-LLT; pravastatin 40 mg versus usual care) and Collaborative Atorvastatin Diabetes Study (CARDS; atorvastatin 10 mg versus placebo) of 3878 and 2838 patients with type 2 diabetes mellitus, respectively. Model calibration was adequate in both external data sets, discrimination was moderate (ALLHAT-LLT: c-statistics, 0.64 [95% confidence interval, 0.61-0.67] and CARDS: 0.68 [95% confidence interval, 0.64-0.72]). CONCLUSIONS: ARRs of major cardiovascular events by statin therapy can be accurately estimated for individual patients with type 2 diabetes mellitus using a model based on routinely available patient characteristics. There is a wide distribution in ARR that may complement informed decision making. CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifier: NCT00327418 (CARDS) and NCT00000542 (ALLHAT).

Primary prevention of cardiovascular disease with atorvastatin in type 2 diabetes in the Collaborative Atorvastatin Diabetes Study (CARDS): multicentre randomised placebo-controlled trial.

BACKGROUND: Type 2 diabetes is associated with a substantially increased risk of cardiovascular disease, but the role of lipid-lowering therapy with statins for the primary prevention of cardiovascular disease in diabetes is inadequately defined. We aimed to assess the effectiveness of atorvastatin 10 mg daily for primary prevention of major cardiovascular events in patients with type 2 diabetes without high concentrations of LDL-cholesterol. METHODS: 2838 patients aged 40-75 years in 132 centres in the UK and Ireland were randomised to placebo (n=1410) or atorvastatin 10 mg daily (n=1428). Study entrants had no documented previous history of cardiovascular disease, an LDL-cholesterol concentration of 4.14 mmol/L or lower, a fasting triglyceride amount of 6.78 mmol/L or less, and at least one of the following: retinopathy, albuminuria, current smoking, or hypertension. The primary endpoint was time to first occurrence of the following: acute coronary heart disease events, coronary revascularisation, or stroke. Analysis was by intention to treat. FINDINGS: The trial was terminated 2 years earlier than expected because the prespecified early stopping rule for efficacy had been met. Median duration of follow-up was 3.9 years (IQR 3.0-4.7). 127 patients allocated placebo (2.46 per 100 person-years at risk) and 83 allocated atorvastatin (1.54 per 100 person-years at risk) had at least one major cardiovascular event (rate reduction 37% [95% CI -52 to -17], p=0.001). Treatment would be expected to prevent at least 37 major vascular events per 1000 such people treated for 4 years. Assessed separately, acute coronary heart disease events were reduced by 36% (-55 to -9), coronary revascularisations by 31% (-59 to 16), and rate of stroke by 48% (-69 to -11). Atorvastatin reduced the death rate by 27% (-48 to 1, p=0.059). No excess of adverse events was noted in the atorvastatin group. INTERPRETATION: Atorvastatin 10 mg daily is safe and efficacious in reducing the risk of first cardiovascular disease events, including stroke, in patients with type 2 diabetes without high LDL-cholesterol. No justification is available for having a particular threshold level of LDL-cholesterol as the sole arbiter of which patients with type 2 diabetes should receive statins. The debate about whether all people with this disorder warrant statin treatment should now focus on whether any patients are at sufficiently low risk for this treatment to be withheld.

Contemporary risk of hip fracture in type 1 and type 2 diabetes: a national registry study from Scotland.

The purpose of this study was to compare contemporary risk of hip fracture in type 1 and type 2 diabetes with the nondiabetic population. Using a national diabetes database, we identified those with type 1 and type 2 diabetes who were aged 20 to 84 years and alive anytime from January 1, 2005 to December 31, 2007. All hospitalized events for hip fracture in 2005 to 2007 for diabetes patients were linked and compared with general population counts. Age- and calendar-year-adjusted incidence rate ratios were calculated by diabetes type and sex. One hundred five hip fractures occurred in 21,033 people (59,585 person-years) with type 1 diabetes; 1421 in 180,841 people (462,120 person-years) with type 2 diabetes; and 11,733 hip fractures over 10,980,599 person-years in the nondiabetic population (3.66 million people). Those with type 1 diabetes had substantially elevated risks of hip fracture compared with the general population incidence risk ratio (IRR) of 3.28 (95% confidence interval [CI] 2.52-4.26) in men and 3.54 (CI 2.75-4.57) in women. The IRR was greater at younger ages, but absolute risk difference was greatest at older ages. In type 2 diabetes, there was no elevation in risk among men (IRR 0.97 [CI 0.92-1.02]) and the increase in risk in women was small (IRR 1.05 [CI 1.01-1.10]). There remains a substantial elevation relative risk of hip fracture in people with type 1 diabetes, but the relative risk is much lower than in earlier studies. In contrast, there is currently little elevation in overall hip fracture risk with type 2 diabetes, but this may mask elevations in risk in particular subgroups of type 2 diabetes patients with different body mass indexes, diabetes duration, or drug exposure.

Effect of socioeconomic status on mortality among people with type 2 diabetes: a study from the Scottish Diabetes Research Network Epidemiology Group.

OBJECTIVE: The study objective was to describe the effect of socioeconomic status (SES) on mortality among people with type 2 diabetes. RESEARCH DESIGN AND METHODS: We used a population-based national electronic diabetes database for 35- to 84-year-olds in Scotland for 2001-2007 linked to mortality records. SES was derived from an area-based measure with Q5 and Q1 representing the most deprived and affluent quintiles, respectively. Poisson regression was used to estimate relative risks (RRs) for mortality among people with type 2 diabetes compared with the population without diabetes stratified by age (35-64 and 65-84 years), sex, duration of diabetes (< 2 and ≥ 2 years), and SES. RESULTS: Complete data were available for 210,994 eligible individuals (99.4%), and there were 33,842 deaths. Absolute mortality from all causes among people with type 2 diabetes increased with increasing age and socioeconomic deprivation and was higher for men than women. RR for mortality associated with type 2 diabetes was highest for women aged 35-64 years in Q1 with diabetes duration < 2 years at 4.83 (95% CI 3.15-7.40) and lowest for men aged 65-84 years in Q5 with diabetes duration ≥ 2 years at 1.13 (1.03-1.24). CONCLUSIONS: SES modifies the association between type 2 diabetes and mortality so that RR for mortality is lower among more deprived populations. Age, sex, and duration of diabetes also interact with type 2 diabetes to influence RR of mortality. Differences in prevalence of comorbidities may explain these findings.