Lifestyle changes and kidney function: A 10-year follow-up study in patients with manifest cardiovascular disease.

BACKGROUND: Patients with cardiovascular disease (CVD) are at higher risk of kidney function decline. The current study aimed to examine the association of lifestyle changes with kidney function decline in patients with manifest CVD. METHODS: A total of 2260 patients from the Utrecht Cardiovascular Cohort-Second Manifestations of ARTerial disease cohort with manifest CVD who returned for a follow-up visit after a median of 9.9 years were included. The relation between change in lifestyle factors (smoking, alcohol consumption, physical activity and obesity) and change in kidney function (eGFR and uACR) was assessed using linear regression models. RESULTS: An increase in body mass index (ß -2.81; 95% CI -3.98; -1.63 per 5 kg/m2 ) and for men also an increase in waist circumference (ß -0.87; 95% CI -1.28; -0.47 per 5 cm) were significantly associated with a steeper decline in eGFR over 10 years. Continuing smoking (ß -2.44, 95% CI -4.43; -0.45) and recent smoking cessation during follow-up (ß -3.27; 95% CI -5.20; -1.34) were both associated with a steeper eGFR decline compared to patients who remained as non- or previous smokers from baseline. No significant association was observed between physical exercise or alcohol consumption and kidney function decline. No significant relation between any lifestyle factor and change in uACR was observed. CONCLUSIONS: In patients with CVD, continuing smoking, recent smoking cessation and an increase in obesity markers were related to a steeper kidney function decline. Although no definite conclusions from this study can be drawn, the results support the importance of encouraging weight loss and smoking cessation in high-risk patients as a means of slowing down kidney function decline.

Individual Treatment Effect Estimation of 2 Doses of Dabigatran on Stroke and Major Bleeding in Atrial Fibrillation.

BACKGROUND: We aimed to estimate absolute benefit and harm from treatment with dabigatran in individual patients with atrial fibrillation, and to select the optimal dose for each individual. METHODS: We derived and validated a prediction model for ischemic stroke/systemic embolism and major bleeding in patients with atrial fibrillation from the 3 treatment arms of the RE-LY trial (Randomized Evaluation of Long-Term Anticoagulation Therapy With Dabigatran Etexilate) (n=11 955 in derivation cohort, n=6158 in validation cohort). Readily available patient characteristics were included in Fine and Gray competing risk models (sex, age, smoking, antiplatelet drugs, previous vascular disease, diabetes mellitus, blood pressure, estimated glomerular filtration rate, and hemoglobin). Five-year risks for ischemic stroke/systemic embolism and major bleeding were estimated without anticoagulation therapy, and compared with high- and low-dose dabigatran. RESULTS: Model calibration was good, and discrimination was adequate with a c-statistic of 0.65 (95% CI, 0.62-0.70) for ischemic stroke/systemic embolism and 0.69 (95% CI, 0.66-0.71) for major bleeding. The 5-year absolute risk reduction for ischemic stroke/systemic embolism with dabigatran 150 mg twice daily ranged from <10% in 20% of patients to >25% in 14% of patients, and the 5-year absolute risk increase for major bleeding ranged from <5% in 53% of patients to 15% to 20% in 1% of patients. Comparing high-dose to low-dose dabigatran, the net benefit (absolute risk reduction minus absolute risk increase) was positive for 46% of patients. CONCLUSIONS: The absolute treatment benefits and harms of dabigatran in atrial fibrillation can be estimated based on readily available patient characteristics. Such treatment effect estimations can be used for shared decision making before starting dabigatran treatment and to determine the optimal dose. CLINICAL TRIAL REGISTRATION: URL: https://www.clinicaltrials.gov . Unique identifier: NCT00262600.

Blood pressure-lowering treatment strategies based on cardiovascular risk versus blood pressure: A meta-analysis of individual participant data.

BACKGROUND: Clinical practice guidelines have traditionally recommended blood pressure treatment based primarily on blood pressure thresholds. In contrast, using predicted cardiovascular risk has been advocated as a more effective strategy to guide treatment decisions for cardiovascular disease (CVD) prevention. We aimed to compare outcomes from a blood pressure-lowering treatment strategy based on predicted cardiovascular risk with one based on systolic blood pressure (SBP) level. METHODS AND FINDINGS: We used individual participant data from the Blood Pressure Lowering Treatment Trialists' Collaboration (BPLTTC) from 1995 to 2013. Trials randomly assigned participants to either blood pressure-lowering drugs versus placebo or more intensive versus less intensive blood pressure-lowering regimens. We estimated 5-y risk of CVD events using a multivariable Weibull model previously developed in this dataset. We compared the two strategies at specific SBP thresholds and across the spectrum of risk and blood pressure levels studied in BPLTTC trials. The primary outcome was number of CVD events avoided per persons treated. We included data from 11 trials (47,872 participants). During a median of 4.0 y of follow-up, 3,566 participants (7.5%) experienced a major cardiovascular event. Areas under the curve comparing the two treatment strategies throughout the range of possible thresholds for CVD risk and SBP demonstrated that, on average, a greater number of CVD events would be avoided for a given number of persons treated with the CVD risk strategy compared with the SBP strategy (area under the curve 0.71 [95% confidence interval (CI) 0.70-0.72] for the CVD risk strategy versus 0.54 [95% CI 0.53-0.55] for the SBP strategy). Compared with treating everyone with SBP ≥ 150 mmHg, a CVD risk strategy would require treatment of 29% (95% CI 26%-31%) fewer persons to prevent the same number of events or would prevent 16% (95% CI 14%-18%) more events for the same number of persons treated. Compared with treating everyone with SBP ≥ 140 mmHg, a CVD risk strategy would require treatment of 3.8% (95% CI 12.5% fewer to 7.2% more) fewer persons to prevent the same number of events or would prevent 3.1% (95% CI 1.5%-5.0%) more events for the same number of persons treated, although the former estimate was not statistically significant. In subgroup analyses, the CVD risk strategy did not appear to be more beneficial than the SBP strategy in patients with diabetes mellitus or established CVD. CONCLUSIONS: A blood pressure-lowering treatment strategy based on predicted cardiovascular risk is more effective than one based on blood pressure levels alone across a range of thresholds. These results support using cardiovascular risk assessment to guide blood pressure treatment decision-making in moderate- to high-risk individuals, particularly for primary prevention.

Estimation of individual beneficial and adverse effects of intensive glucose control for patients with type 2 diabetes.

AIMS/HYPOTHESIS: Intensive glucose control reduces the risk of vascular complications while increasing the risk of severe hypoglycaemia at a group level. We sought to estimate individual beneficial and adverse effects of intensive glucose control in patients with type 2 diabetes. METHODS: We performed a post hoc analysis of the Action in Diabetes and Vascular Disease: Preterax and Diamicron MR Controlled Evaluation (ADVANCE) trial, a randomised controlled trial evaluating standard vs intensive glucose control (HbA1c target ≤6.5% [48 mmol/mol]). In 11,140 participants, we estimated the individual 5 year absolute risk reduction (ARR) for the composite outcome of major micro- and macrovascular events and absolute risk increase (ARI) for severe hypoglycaemia for intensive vs standard glucose control. Predictions were based on competing risks models including clinical characteristics and randomised treatment. RESULTS: Based on these models, 76% of patients had a substantial estimated 5 year ARR for major vascular events (>1%, 5 year number-needed-to-benefit [NNTB5] <100) and 1% had a small ARR (<0.5%, NNTB5 >200). Similarly, 36% of patients had a substantial estimated ARI for severe hypoglycaemia (5 year number-needed-to-harm [NNTH5] <100) and 29% had a small ARI (NNTH5 >200). When assigning similar or half the weight to severe hypoglycaemia compared with a major vascular event, net benefit was positive in 85% or 99% of patients, respectively. Limiting intensive treatment to the 85% patient subgroup had no significant effect on the overall incidence of major vascular events and severe hypoglycaemia compared with treating all patients. CONCLUSIONS/INTERPRETATION: Taking account of the effects of intensive glucose control on major micro- and macrovascular events and severe hypoglycaemia for individual patients, the estimated net benefit was positive in the majority of the participants in the ADVANCE trial. The estimated individual effects can inform treatment decisions once individual weights assigned to positive and adverse effects have been specified. TRIAL REGISTRATION: ClinicalTrials.gov NCT00145925.

Personalized cardiovascular disease prevention by applying individualized prediction of treatment effects.

Large-scale randomized clinical trials have established the efficacy of cholesterol-lowering, blood pressure-lowering, and anti-platelet therapy to prevent cardiovascular diseases. A challenge for clinicians is to apply group-level evidence from these trials to individual patients. Trials typically report a single treatment effect estimate which is the average effect of all participants, comprising patients who respond poorly, intermediately, and well. Clinicians would preferably make patient-tailored treatment decisions. Therefore, one would require an estimate of an individual patient's response to therapy. Although not yet widely recognized, trials contain this type of information. In this paper, we show how available information from landmark trials can be translated to an individual 'treatment score' through the use of multivariable therapeutic prediction models. These models provide an individual estimate of the absolute risk reduction in cardiovascular events given the specific combination of multiple clinical characteristics of a patient under care. Based on this individualized treatment estimate and metrics such as the individual number-needed-to-treat, clinicians together with their patients can decide whether drug treatment or what treatment intensity is worthwhile. Selective treatment of those who can anticipate the greatest benefit and the least harm on an individualized basis could reduce the number of unnecessary treatments and healthcare costs beyond that currently achievable by subgroup analyses based on single patient characteristics.

One Heartbeat Away from a Prediction Model for Cardiovascular Diseases in Patients with Chronic Kidney Disease: A Systematic Review.

INTRODUCTION: Patients with chronic kidney disease (CKD) have a high risk of cardiovascular disease (CVD). Prediction models, combining clinical and laboratory characteristics, are commonly used to estimate an individual's CVD risk. However, these models are not specifically developed for patients with CKD and may therefore be less accurate. In this review, we aim to give an overview of CVD prognostic studies available, and their methodological quality, specifically for patients with CKD. METHODS: MEDLINE was searched for papers reporting CVD prognostic studies in patients with CKD published between 2012 and 2021. Characteristics regarding patients, study design, outcome measurement, and prediction models were compared between included studies. The risk of bias of studies reporting on prognostic factors or the development/validation of a prediction model was assessed with, respectively, the QUIPS and PROBAST tool. RESULTS: In total, 134 studies were included, of which 123 studies tested the incremental value of one or more predictors to existing models or common risk factors, while only 11 studies reported on the development or validation of a prediction model. Substantial heterogeneity in cohort and study characteristics, such as sample size, event rate, and definition of outcome measurements, was observed across studies. The most common predictors were age (87%), sex (75%), diabetes (70%), and estimated glomerular filtration rate (69%). Most of the studies on prognostic factors have methodological shortcomings, mostly due to a lack of reporting on clinical and methodological information. Of the 11 studies on prediction models, six developed and internally validated a model and four externally validated existing or developed models. Only one study on prognostic models showed a low risk of bias and high applicability. CONCLUSION: A large quantity of prognostic studies has been published, yet their usefulness remains unclear due to incomplete presentation, and lack of external validation of prognostic models. Our review can be used to select the most appropriate prognostic model depending on the patient population, outcome, and risk of bias. Future collaborative efforts should aim at improving existing models by externally validating them, evaluating the addition of new predictors, and assessment of the clinical impact. REGISTRATION: We have registered the protocol of our systematic review on PROSPERO (CRD42021228043).

Including measures of chronic kidney disease to improve cardiovascular risk prediction by SCORE2 and SCORE2-OP.

AIMS: The 2021 European Society of Cardiology (ESC) guideline on cardiovascular disease (CVD) prevention categorizes moderate and severe chronic kidney disease (CKD) as high and very-high CVD risk status regardless of other factors like age and does not include estimated glomerular filtration rate (eGFR) and albuminuria in its algorithms, systemic coronary risk estimation 2 (SCORE2) and systemic coronary risk estimation 2 in older persons (SCORE2-OP), to predict CVD risk. We developed and validated an 'Add-on' to incorporate CKD measures into these algorithms, using a validated approach. METHODS: In 3,054 840 participants from 34 datasets, we developed three Add-ons [eGFR only, eGFR + urinary albumin-to-creatinine ratio (ACR) (the primary Add-on), and eGFR + dipstick proteinuria] for SCORE2 and SCORE2-OP. We validated C-statistics and net reclassification improvement (NRI), accounting for competing risk of non-CVD death, in 5,997 719 participants from 34 different datasets. RESULTS: In the target population of SCORE2 and SCORE2-OP without diabetes, the CKD Add-on (eGFR only) and CKD Add-on (eGFR + ACR) improved C-statistic by 0.006 (95%CI 0.004-0.008) and 0.016 (0.010-0.023), respectively, for SCORE2 and 0.012 (0.009-0.015) and 0.024 (0.014-0.035), respectively, for SCORE2-OP. Similar results were seen when we included individuals with diabetes and tested the CKD Add-on (eGFR + dipstick). In 57 485 European participants with CKD, SCORE2 or SCORE2-OP with a CKD Add-on showed a significant NRI [e.g. 0.100 (0.062-0.138) for SCORE2] compared to the qualitative approach in the ESC guideline. CONCLUSION: Our Add-ons with CKD measures improved CVD risk prediction beyond SCORE2 and SCORE2-OP. This approach will help clinicians and patients with CKD refine risk prediction and further personalize preventive therapies for CVD.

Development and Validation of a Lifetime Risk Model for Kidney Failure and Treatment Benefit in Type 2 Diabetes: 10-Year and Lifetime Risk Prediction Models.

BACKGROUND AND OBJECTIVES: Individuals with type 2 diabetes are at a higher risk of developing kidney failure. The objective of this study was to develop and validate a decision support tool for estimating 10-year and lifetime risks of kidney failure in individuals with type 2 diabetes as well as estimating individual treatment effects of preventive medication. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: The prediction algorithm was developed in 707,077 individuals with prevalent and incident type 2 diabetes from the Swedish National Diabetes Register for 2002-2019. Two Cox proportional regression functions for kidney failure (first occurrence of kidney transplantation, long-term dialysis, or persistent eGFR <15 ml/min per 1.73 m2) and all-cause mortality as respective end points were developed using routinely available predictors. These functions were combined into life tables to calculate the predicted survival without kidney failure while using all-cause mortality as the competing outcome. The model was externally validated in 256,265 individuals with incident type 2 diabetes from the Scottish Care Information Diabetes database between 2004 and 2019. RESULTS: During a median follow-up of 6.8 years (interquartile range, 3.2-10.6), 8004 (1%) individuals with type 2 diabetes in the Swedish National Diabetes Register cohort developed kidney failure, and 202,078 (29%) died. The model performed well, with c statistics for kidney failure of 0.89 (95% confidence interval, 0.88 to 0.90) for internal validation and 0.74 (95% confidence interval, 0.73 to 0.76) for external validation. Calibration plots showed good agreement in observed versus predicted 10-year risk of kidney failure for both internal and external validation. CONCLUSIONS: This study derived and externally validated a prediction tool for estimating 10-year and lifetime risks of kidney failure as well as life years free of kidney failure gained with preventive treatment in individuals with type 2 diabetes using easily available clinical predictors. PODCAST: This article contains a podcast at https://dts.podtrac.com/redirect.mp3/www.asn-online.org/media/podcast/CJASN/2022_11_04_CJN05020422.mp3.

End-stage kidney disease in patients with clinically manifest vascular disease; incidence and risk factors: results from the UCC-SMART cohort study.

BACKGROUND: Patients with cardiovascular disease (CVD) are at increased risk of end-stage kidney disease (ESKD). Insights into the incidence and role of modifiable risk factors for end-stage kidney disease may provide means for prevention in patients with cardiovascular disease. METHODS: We included 8402 patients with stable cardiovascular disease. Incidence rates (IRs) for end-stage kidney disease were determined stratified according to vascular disease location. Cox proportional hazard models were used to assess the risk of end-stage kidney disease for the different determinants. RESULTS: Sixty-five events were observed with a median follow-up of 8.6 years. The overall incidence rate of end-stage kidney disease was 0.9/1000 person-years. Patients with polyvascular disease had the highest incidence rate (1.8/1000 person-years). Smoking (Hazard ratio (HR) 1.87; 95% CI 1.10-3.19), type 2 diabetes (HR 1.81; 95% CI 1.05-3.14), higher systolic blood pressure (HR 1.37; 95% CI 1.24-1.52/10 mmHg), lower estimated glomerular filtration rate (eGFR) (HR 2.86; 95% CI 2.44-3.23/10 mL/min/1.73 m2) and higher urine albumin/creatinine ratio (uACR) (HR 1.19; 95% CI 1.15-1.23/10 mg/mmol) were independently associated with elevated risk of end-stage kidney disease. Body mass index (BMI), waist circumference, non-HDL-cholesterol and exercise were not independently associated with risk of end-stage kidney disease. CONCLUSIONS: Incidence of end-stage kidney disease in patients with cardiovascular disease varies according to vascular disease location. Several modifiable risk factors for end-stage kidney disease were identified in patients with cardiovascular disease. These findings highlight the potential of risk factor management in patients with manifest cardiovascular disease.

Separating the effects of 24-hour urinary chloride and sodium excretion on blood pressure and risk of hypertension: Results from PREVEND.

OBJECTIVE: Research into dietary factors associated with hypertension has focused on the sodium component of salt. However, chloride has distinct physiological effects that may surpass the effect of sodium on blood pressure. This study aims to separate the specific effects of chloride and sodium intake on blood pressure. METHODS: We studied 5673 participants from the Prevention of Renal and Vascular End-Stage Disease(PREVEND) study. Urinary chloride(uCl) and sodium(uNa) were measured in two 24-hour collections. We used generalized-linear-regression to evaluate the relation of uCl and uNa with baseline blood pressure and Cox-proportional-hazards-analysis to assess the association with hypertension. Multicollinearity was assessed with Ridge regression. RESULTS: Baseline 24-hour uCl was 135±39mmol and uNa was 144±54mmol. The correlation between uCl and uNa was high (Pearson's r = 0.96). UCl and uNa had similar non-significant positive and linear associations with blood pressure. In 3515 normotensive patients, 1021 patients developed hypertension during a median follow-up of 7.4 years. UCl and uNa had a comparable but non-significant J-shaped effect on the risk of hypertension. Adding both uCl and uNa to the same model produced instability, demonstrated by Ridge coefficients that converged or changed sign. The single index of uNa minus uCl showed a non-significant higher risk of hypertension of 2% per 10mmol/24-hour difference (HR1.02, 95%CI 0.98-1.06). CONCLUSION: UCl and uNa had similar positive but non-significant associations with blood pressure and risk of hypertension and their effects could not be disentangled. Hence, the alleged adverse effects of high salt intake could be due to sodium, chloride or both. This encourages further study into the effect of chloride in order to complement dietary recommendations currently focused on sodium alone.

Incorporating kidney disease measures into cardiovascular risk prediction: Development and validation in 9 million adults from 72 datasets.

BACKGROUND: Chronic kidney disease (CKD) measures (estimated glomerular filtration rate [eGFR] and albuminuria) are frequently assessed in clinical practice and improve the prediction of incident cardiovascular disease (CVD), yet most major clinical guidelines do not have a standardized approach for incorporating these measures into CVD risk prediction. "CKD Patch" is a validated method to calibrate and improve the predicted risk from established equations according to CKD measures. METHODS: Utilizing data from 4,143,535 adults from 35 datasets, we developed several "CKD Patches" incorporating eGFR and albuminuria, to enhance prediction of risk of atherosclerotic CVD (ASCVD) by the Pooled Cohort Equation (PCE) and CVD mortality by Systematic COronary Risk Evaluation (SCORE). The risk enhancement by CKD Patch was determined by the deviation between individual CKD measures and the values expected from their traditional CVD risk factors and the hazard ratios for eGFR and albuminuria. We then validated this approach among 4,932,824 adults from 37 independent datasets, comparing the original PCE and SCORE equations (recalibrated in each dataset) to those with addition of CKD Patch. FINDINGS: We confirmed the prediction improvement with the CKD Patch for CVD mortality beyond SCORE and ASCVD beyond PCE in validation datasets (Δc-statistic 0.027 [95% CI 0.018-0.036] and 0.010 [0.007-0.013] and categorical net reclassification improvement 0.080 [0.032-0.127] and 0.056 [0.044-0.067], respectively). The median (IQI) of the ratio of predicted risk for CVD mortality with CKD Patch vs. the original prediction with SCORE was 2.64 (1.89-3.40) in very high-risk CKD (e.g., eGFR 30-44 ml/min/1.73m2 with albuminuria ≥30 mg/g), 1.86 (1.48-2.44) in high-risk CKD (e.g., eGFR 45-59 ml/min/1.73m2 with albuminuria 30-299 mg/g), and 1.37 (1.14-1.69) in moderate risk CKD (e.g., eGFR 60-89 ml/min/1.73m2 with albuminuria 30-299 mg/g), indicating considerable risk underestimation in CKD with SCORE. The corresponding estimates for ASCVD with PCE were 1.55 (1.37-1.81), 1.24 (1.10-1.54), and 1.21 (0.98-1.46). INTERPRETATION: The "CKD Patch" can be used to quantitatively enhance ASCVD and CVD mortality risk prediction equations recommended in major US and European guidelines according to CKD measures, when available. FUNDING: US National Kidney Foundation and the NIDDK.

Novel Biomarkers to Improve the Prediction of Cardiovascular Event Risk in Type 2 Diabetes Mellitus.

BACKGROUND: We evaluated the ability of 23 novel biomarkers representing several pathophysiological pathways to improve the prediction of cardiovascular event (CVE) risk in patients with type 2 diabetes mellitus beyond traditional risk factors. METHODS AND RESULTS: We used data from 1002 patients with type 2 diabetes mellitus from the Second Manifestations of ARTertial disease (SMART) study and 288 patients from the European Prospective Investigation into Cancer and Nutrition-NL (EPIC-NL). The associations of 23 biomarkers (adiponectin, C-reactive protein, epidermal-type fatty acid binding protein, heart-type fatty acid binding protein, basic fibroblast growth factor, soluble FMS-like tyrosine kinase-1, soluble intercellular adhesion molecule-1 and -3, matrix metalloproteinase [MMP]-1, MMP-3, MMP-9, N-terminal prohormone of B-type natriuretic peptide, osteopontin, osteonectin, osteocalcin, placental growth factor, serum amyloid A, E-selectin, P-selectin, tissue inhibitor of MMP-1, thrombomodulin, soluble vascular cell adhesion molecule-1, and vascular endothelial growth factor) with CVE risk were evaluated by using Cox proportional hazards analysis adjusting for traditional risk factors. The incremental predictive performance was assessed with use of the c-statistic and net reclassification index (NRI; continuous and based on 10-year risk strata 0-10%, 10-20%, 20-30%, >30%). A multimarker model was constructed comprising those biomarkers that improved predictive performance in both cohorts. N-terminal prohormone of B-type natriuretic peptide, osteopontin, and MMP-3 were the only biomarkers significantly associated with an increased risk of CVE and improved predictive performance in both cohorts. In SMART, the combination of these biomarkers increased the c-statistic with 0.03 (95% CI 0.01-0.05), and the continuous NRI was 0.37 (95% CI 0.21-0.52). In EPIC-NL, the multimarker model increased the c-statistic with 0.03 (95% CI 0.00-0.03), and the continuous NRI was 0.44 (95% CI 0.23-0.66). Based on risk strata, the NRI was 0.12 (95% CI 0.03-0.21) in SMART and 0.07 (95% CI -0.04-0.17) in EPIC-NL. CONCLUSIONS: Of the 23 evaluated biomarkers from different pathophysiological pathways, N-terminal prohormone of B-type natriuretic peptide, osteopontin, MMP-3, and their combination improved CVE risk prediction in 2 separate cohorts of patients with type 2 diabetes mellitus beyond traditional risk factors. However, the number of patients reclassified to a different risk stratum was limited.

[Is morphine indicated in patients with cardiac asthma? Indications for negative effects from the use of morphine]. / Morfine geïndiceerd bij patiënten met astma cardiale? Aanwijzingen voor negatieve effecten van morfine.

According to current Dutch and European guidelines, morphine has a place in the treatment of patients with cardiac asthma. There are no studies showing an improvement in objective parameters with the use of morphine in patients with cardiac asthma. Recent large retrospective cohort studies indicate an increased mortality risk among patients given morphine in the treatment of cardiac asthma, even after correction for confounders. In addition, morphine may be linked to an increased risk of intensive care unit admission. Morphine should be used with caution in patients with cardiac asthma.

Predicting the effects of blood pressure-lowering treatment on major cardiovascular events for individual patients with type 2 diabetes mellitus: results from Action in Diabetes and Vascular Disease: Preterax and Diamicron MR Controlled Evaluation.

UNLABELLED: Blood pressure-lowering treatment reduces cardiovascular risk in patients with diabetes mellitus, but the effect varies between individuals. We sought to identify which patients benefit most from such treatment in a large clinical trial in type 2 diabetes mellitus. In Action in Diabetes and Vascular Disease: Preterax and Diamicron MR Controlled Evaluation (ADVANCE) participants (n=11 140), we estimated the individual patient 5-year absolute risk of major adverse cardiovascular events with and without treatment by perindopril-indapamide (4/1.25 mg). The difference between treated and untreated risk is the estimated individual patient's absolute risk reduction (ARR). Predictions were based on a Cox proportional hazards model inclusive of demographic and clinical characteristics together with the observed relative treatment effect. The group-level effect of selectively treating patients with an estimated ARR above a range of decision thresholds was compared with treating everyone or those with a blood pressure >140/90 mm Hg using net benefit analysis. In ADVANCE, there was wide variation in treatment effects across individual patients. According to the algorithm, 43% of patients had a large predicted 5-year ARR of ≥1% (number-needed-to-treat [NNT5] ≤100) and 40% had an intermediate predicted ARR of 0.5% to 1% (NNT5=100-`200). The proportion of patients with a small ARR of ≤0.5% (NNT5≥200) was 17%. Provided that one is prepared to treat at most 200 patients for 5 years to prevent 1 adverse outcome, prediction-based treatment yielded the highest net benefit. In conclusion, a multivariable treatment algorithm can identify those individuals who benefit most from blood pressure-lowering therapy in terms of ARR of major adverse cardiovascular events and may be used to guide treatment decisions in individual patients with diabetes. CLINICAL TRIAL REGISTRATION URL: http://www.clinicaltrials.gov. Unique identifier: NCT00145925.

The relation between HbA1c and cardiovascular events in patients with type 2 diabetes with and without vascular disease.

OBJECTIVE: Poor glycemic control is related to vascular events in patients with type 2 diabetes, but the presence of vascular disease might influence this relation. We evaluated the relation between glycemic control (HbA1c level) and new cardiovascular events and mortality in patients with type 2 diabetes, with and without vascular disease. RESEARCH DESIGN AND METHODS: In a cohort of 1,687 patients with type 2 diabetes enrolled in the Second Manifestations of Arterial Disease (SMART) study, the continuous relation between HbA1c and cardiovascular events (composite of myocardial infarction, stroke, and vascular mortality) and all-cause mortality was evaluated with Cox proportional hazard analyses stratified for the presence of vascular disease. RESULTS: During a median follow-up time of 6.1 years (interquartile range 3.1-9.5 years), a new cardiovascular event developed in 293 patients and 340 patients died. In all patients, the hazard ratio (HR) of the relation between HbA1c level and cardiovascular events was 1.06 (95% CI 0.97-1.17). A 1 percentage point higher HbA1c level was related to a 27% higher risk of a cardiovascular event in patients with type 2 diabetes without vascular disease (HR 1.27 [95% CI 1.06-1.51]), but not in patients with vascular disease (HR 1.03 [95% CI 0.93-1.15], P for interaction = 0.195). A 1 percentage point higher HbA1c level was related to a 16% higher risk of death (HR 1.16 [95% CI 1.06-1.28]) in patients with vascular disease and a nonsignificant 13% higher risk of all-cause mortality (HR 1.13 [95% CI 0.97-1.31]) in patients without vascular disease. CONCLUSIONS: In patients with type 2 diabetes, there is a modest, but not statistically significant, relation between HbA1c level and cardiovascular events, and, as there was no statistically significant interaction, this relation was not different for patients with or without clinical manifestation of vascular disease.

Prediction of absolute risk reduction of cardiovascular events with perindopril for individual patients with stable coronary artery disease - results from EUROPA.

BACKGROUND: Angiotensin-converting-enzyme inhibition reduces the risk of cardiovascular events at a group level. Presumably, the absolute effect of treatment varies between individuals. We sought to develop multivariable prediction scores to estimate individual treatment effect of perindopril in patients with stable coronary artery disease (sCAD). METHODS: In EUROPA trial participants, we estimated the individual patient 5-year absolute risk reduction (ARR) of major adverse cardiovascular events(MACE) by perindopril. Predictions were based on a new Coxproportional-hazards model with clinical characteristics and an external risk score in combination with the observed relative risk reduction. Second, a genetic profile modifying the relative efficacy of perindopril was added. The individual patient ARR was defined as the difference in MACE risk with and without treatment. The group level impact of selectively treating patients with the largest predicted treatment effect was evaluated using net benefit analysis. RESULTS: The risk score combining clinical and genetic characteristics estimated the 5-year absolute treatment effect to be absent or adverse in 27% of patients. On the other hand, the risk score estimated a small 5-year ARR of ≤2% (NNT5≥50) in 20% of patients, a modest ARR of 2-4% (NNT5 25-50) in 26%, and a large ARR of ≥4% (NNT5≤25) in 28%. The external risk score yielded similar predictions. Selective prediction-based treatment resulted in higher net benefit compared to treat everyone at any treatment threshold. CONCLUSION: A prediction score combining clinical characteristics and genetic information can quantify the ARR of MACE by perindopril for individual patients with sCAD and may be used to guide treatment decisions. TRIAL REGISTRATION NUMBER: ISRCTN37166280.

Age-related differences in abdominal fat distribution in premenopausal and postmenopausal women with cardiovascular disease.

OBJECTIVE: The aim of this study was to determine the effect of sex and the menopausal transition on age-related differences in visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) among patients with cardiovascular disease. METHODS: A cross-sectional study of 997 women and 3,409 men with cardiovascular disease was performed. VAT and SAT were measured by ultrasonography. Differences in abdominal fat per decade in premenopausal and postmenopausal women were analyzed with linear regression and compared with men younger and older than the mean menopause age of women. RESULTS: VAT increased gradually across advancing age groups. For postmenopausal women, the 10-year differences in VAT were smaller than those for premenopausal women (0.24 cm [ß = 0.24; 95% CI, 0.05 to 0.43] vs 0.71 cm [ß = 0.71; 95% CI, 0.29 to 1.12]). There were no differences in SAT (ß = -0.12; 95% CI, -0.37 to 0.13) in premenopausal women, and SAT decreased across the age groups of postmenopausal women (-0.36 cm per decade [ß = -0.36; 95% CI, -0.47 to -0.26]). Postmenopausal women showed 10-year differences in VAT that were larger than those for men 48 years or older (0.24 cm per decade [ß = 0.24; 95% CI, 0.05 to 0.43] vs -0.01 cm per decade [ß = -0.01; 95% CI, -0.12 to 0.10]). In addition, 10-year differences in SAT were larger in postmenopausal women than in men 48 years or older (-0.36 cm [ß = -0.36; 95% CI, -0.47 to -0.26] vs -0.22 cm [ß = -0.22; 95% CI, -0.27 to -0.18]). CONCLUSIONS: Menopause is not associated with accelerated fat gain in women with cardiovascular disease. Compared with similar-aged men, postmenopausal women show a steeper increase in VAT and a steeper decrease in SAT. These ongoing changes might add to an unfavorable metabolic profile associated with an increased risk of recurrent cardiovascular events.