An Obesity Dietary Quality Index Predicts Abdominal Obesity in Women: Potential Opportunity for New Prevention and Treatment Paradigms

Background. Links between dietary quality and abdominal obesity are poorly understood. Objective. To examine the association between an obesity-specific dietary quality index and abdominal obesity risk in women. Methods. Over 12 years, we followed 288 Framingham Offspring/Spouse Study women, aged 30–69 years, without metabolic syndrome risk factors, cardiovascular disease, cancer, or diabetes at baseline. An 11-nutrient obesity-specific dietary quality index was derived using mean ranks of nutrient intakes from 3-day dietary records. Abdominal obesity (waist circumference >88 cm) was assessed during follow-up. Results. Using multiple logistic regression, women with poorer dietary quality were more likely to develop abdominal obesity compared to those with higher dietary quality (OR 1.87; 95% CI, 1.01, 3.47; P for trend = .048) independent of age, physical activity, smoking, and menopausal status. Conclusions. An obesity-specific dietary quality index predicted abdominal obesity in women, suggesting targets for dietary quality assessment, intervention, and treatment to address abdominal adiposity

ASCORE: an up-to-date cardiovascular risk score for hypertensive patients reflecting contemporary clinical practice developed using the (ASCOT-BPLA) trial data.

A number of risk scores already exist to predict cardiovascular (CV) events. However, scores developed with data collected some time ago might not accurately predict the CV risk of contemporary hypertensive patients that benefit from more modern treatments and management. Using data from the randomised clinical trial Anglo-Scandinavian Cardiac Outcomes Trial-BPLA, with 15 955 hypertensive patients without previous CV disease receiving contemporary preventive CV management, we developed a new risk score predicting the 5-year risk of a first CV event (CV death, myocardial infarction or stroke). Cox proportional hazard models were used to develop a risk equation from baseline predictors. The final risk model (ASCORE) included age, sex, smoking, diabetes, previous blood pressure (BP) treatment, systolic BP, total cholesterol, high-density lipoprotein-cholesterol, fasting glucose and creatinine baseline variables. A simplified model (ASCORE-S) excluding laboratory variables was also derived. Both models showed very good internal validity. User-friendly integer score tables are reported for both models. Applying the latest Framingham risk score to our data significantly overpredicted the observed 5-year risk of the composite CV outcome. We conclude that risk scores derived using older databases (such as Framingham) may overestimate the CV risk of patients receiving current BP treatments; therefore, 'updated' risk scores are needed for current patients

Prognostic Value of Stress Myocardial Perfusion Positron Emission Tomography: Results From A Multicenter Observational Registry

ObjectivesThe primary objective of this multicenter registry was to study the prognostic value of positron emission tomography (PET) myocardial perfusion imaging (MPI) and the improved classification of risk in a large cohort of patients with suspected or known coronary artery disease (CAD).BackgroundLimited prognostic data are available for MPI with PET.MethodsA total of 7,061 patients from 4 centers underwent a clinically indicated rest/stress rubidium-82 PET MPI, with a median follow-up of 2.2 years. The primary outcome of this study was cardiac death (n = 169), and the secondary outcome was all-cause death (n = 570). Net reclassification improvement (NRI) and integrated discrimination analyses were performed.ResultsRisk-adjusted hazard of cardiac death increased with each 10% myocardium abnormal with mildly, moderately, or severely abnormal stress PET (hazard ratio [HR]: 2.3 [95% CI: 1.4 to 3.8; p = 0.001], HR: 4.2 [95% CI: 2.3 to 7.5; p < 0.001], and HR: 4.9 [95% CI: 2.5 to 9.6; p < 0.0001], respectively [normal MPI: referent]). Addition of percent myocardium ischemic and percent myocardium scarred to clinical information (age, female sex, body mass index, history of hypertension, diabetes, dyslipidemia, smoking, angina, beta-blocker use, prior revascularization, and resting heart rate) improved the model performance (C-statistic 0.805 [95% CI: 0.772 to 0.838] to 0.839 [95% CI: 0.809 to 0.869]) and risk reclassification for cardiac death (NRI 0.116 [95% CI: 0.021 to 0.210]), with smaller improvements in risk assessment for all-cause death.ConclusionsIn patients with known or suspected CAD, the extent and severity of ischemia and scar on PET MPI provided powerful and incremental risk estimates of cardiac death and all-cause death compared with traditional coronary risk factors

Genetic Risk Reclassification for Type 2 Diabetes by Age Below or Above 50 Years Using 40 Type 2 Diabetes Risk Single Nucleotide Polymorphisms

OBJECTIVE: To test if knowledge of type 2 diabetes genetic variants improves disease prediction. RESEARCH DESIGN AND METHODS: We tested 40 single nucleotide polymorphisms (SNPs) associated with diabetes in 3,471 Framingham Offspring Study subjects followed over 34 years using pooled logistic regression models stratified by age (<50 years, diabetes cases = 144; or ≥50 years, diabetes cases = 302). Models included clinical risk factors and a 40-SNP weighted genetic risk score. RESULTS: In people <50 years of age, the clinical risk factors model C-statistic was 0.908; the 40-SNP score increased it to 0.911 (P = 0.3; net reclassification improvement (NRI): 10.2%, P = 0.001). In people ≥50 years of age, the C-statistics without and with the score were 0.883 and 0.884 (P = 0.2; NRI: 0.4%). The risk per risk allele was higher in people <50 than ≥50 years of age (24 vs. 11%; P value for age interaction = 0.02). CONCLUSIONS: Knowledge of common genetic variation appropriately reclassifies younger people for type 2 diabetes risk beyond clinical risk factors but not older people

Sex differences in the association between plasma copeptin and incident type 2 diabetes: the Prevention of Renal and Vascular Endstage Disease (PREVEND) study

AIMS/HYPOTHESIS: Vasopressin plays a role in osmoregulation, glucose homeostasis and inflammation. Therefore, plasma copeptin, the stable C-terminal portion of the precursor of vasopressin, has strong potential as a biomarker for the cardiometabolic syndrome and diabetes. Previous results were contradictory, which may be explained by differences between men and women in responsiveness of the vasopressin system. The aim of this study was to evaluate the usefulness of copeptin for prediction of future type 2 diabetes in men and women separately. METHODS: From the Prevention of Renal and Vascular Endstage Disease (PREVEND) study, 4,063 women and 3,909 men without diabetes at baseline were included. A total of 208 women and 288 men developed diabetes during a median follow-up of 7.7 years. RESULTS: In multivariable-adjusted models, we observed a stronger association of copeptin with risk of future diabetes in women (OR 1.49 [95% CI 1.24, 1.79]) than in men (OR 1.01 [95% CI 0.85, 1.19]) (p (interaction) < 0.01). The addition of copeptin to the Data from the Epidemiological Study on the Insulin Resistance Syndrome (DESIR) clinical model improved the discriminative value (C-statistic,+0.007, p = 0.02) and reclassification (integrated discrimination improvement [IDI] = 0.004, p < 0.01) in women. However, we observed no improvement in men. The additive value of copeptin in women was maintained when other independent predictors, such as glucose, high sensitivity C-reactive protein (hs-CRP) and 24 h urinary albumin excretion (UAE), were included in the model. CONCLUSIONS/INTERPRETATION: The association of plasma copeptin with the risk of developing diabetes was stronger in women than in men. Plasma copeptin alone, and along with existing biomarkers (glucose, hs-CRP and UAE), significantly improved the risk prediction for diabetes in women

The Procedural Index for Mortality Risk (PIMR): an index calculated using administrative data to quantify the independent influence of procedures on risk of hospital death

<p>Abstract</p> <p>Background</p> <p>Surgeries and other procedures can influence the risk of death in hospital. All published scales that predict post-operative death risk require clinical data and cannot be measured using administrative data alone. This study derived and internally validated an index that can be calculated using administrative data to quantify the independent risk of hospital death after a procedure.</p> <p>Methods</p> <p>For all patients admitted to a single academic centre between 2004 and 2009, we estimated the risk of all-cause death using the Kaiser Permanente Inpatient Risk Adjustment Methodology (KP-IRAM). We determined whether each patient underwent one of 503 commonly performed therapeutic procedures using Canadian Classification of Interventions codes and whether each procedure was emergent or elective. Multivariate logistic regression modeling was used to measure the association of each procedure-urgency combination with death in hospital independent of the KP-IRAM risk of death. The final model was modified into a scoring system to quantify the independent influence each procedure had on the risk of death in hospital.</p> <p>Results</p> <p>275 460 hospitalizations were included (137,730 derivation, 137,730 validation). In the derivation group, the median expected risk of death was 0.1% (IQR 0.01%-1.4%) with 4013 (2.9%) dying during the hospitalization. 56 distinct procedure-urgency combinations entered our final model resulting in a Procedural Index for Mortality Rating (PIMR) score values ranging from -7 to +11. In the validation group, the PIMR score significantly predicted the risk of death by itself (c-statistic 67.3%, 95% CI 66.6-68.0%) and when added to the KP-IRAM model (c-index improved significantly from 0.929 to 0.938).</p> <p>Conclusions</p> <p>We derived and internally validated an index that uses administrative data to quantify the independent association of a broad range of therapeutic procedures with risk of death in hospital. This scale will improve risk adjustment when administrative data are used for analyses.</p

Age at quitting smoking as a predictor of risk of cardiovascular disease incidence independent of smoking status, time since quitting and pack-years

Background: Risk prediction for CVD events has been shown to vary according to current smoking status, pack-years smoked over a lifetime, time since quitting and age at quitting. The latter two are closely and inversely related. It is not known whether the age at which one quits smoking is an additional important predictor of CVD events. The aim of this study was to determine whether the risk of CVD events varied according to age at quitting after taking into account current smoking status, lifetime pack-years smoked and time since quitting. Findings. We used the Cox proportional hazards model to evaluate the risk of developing a first CVD event for a cohort of participants in the Framingham Offspring Heart Study who attended the fourth examination between ages 30 and 74 years and were free of CVD. Those who quit before the median age of 37 years had a risk of CVD incidence similar to those who were never smokers. The incorporation of age at quitting in the smoking variable resulted in better prediction than the model which had a simple current smoker/non-smoker measure and the one that incorporated both time since quitting and pack-years. These models demonstrated good discrimination, calibration and global fit. The risk among those quitting more than 5 years prior to the baseline exam and those whose age at quitting was prior to 44 years was similar to the risk among never smokers. However, the risk among those quitting less than 5 years prior to the baseline exam and those who continued to smoke until 44 years of age (or beyond) was two and a half times higher than that of never smokers. Conclusions: Age at quitting improves the prediction of risk of CVD incidence even after other smoking measures are taken into account. The clinical benefit of adding age at quitting to the model with other smoking measures may be greater than the associated costs. Thus, age at quitting should be considered in addition to smoking status, time since quitting and pack-years when counselling individuals about their cardiovascular risk

PredictABEL: an R package for the assessment of risk prediction models

The rapid identification of genetic markers for multifactorial diseases from genome-wide association studies is fuelling interest in investigating the predictive ability and health care utility of genetic risk models. Various measures are available for the assessment of risk prediction models, each addressing a different aspect of performance and utility. We developed PredictABEL, a package in R that covers descriptive tables, measures and figures that are used in the analysis of risk prediction studies such as measures of model fit, predictive ability and clinical utility, and risk distributions, calibration plot and the receiver operating characteristic plot. Tables and figures are saved as separate files in a user-specified format, which include publication-quality EPS and TIFF formats. All figures are available in a ready-made layout, but they can be customized to the preferences of the user. The package has been developed for the analysis of genetic risk prediction studies, but can also be used for studies that only include non-genetic risk factors. PredictABEL is freely available at the websites of GenABEL (http://www.genabel.org) and CRAN (http://cran.r-project.org/)

Postprandial Glucose Improves the Risk Prediction of Cardiovascular Death Beyond the Metabolic Syndrome in the Nondiabetic Population

OBJECTIVE - With increasing evidence about the cardiovascular risk associated with postprandial nonfasting glucose and lipid dysmetabolism, it remains uncertain whether the postprandial glucose concentration increases the ability of metabolic syndrome to predict cardiovascular events. RESEARCH DESIGN AND METHODS - This was an observational study of 15, 145 individuals aged 35-75 years without diabetes or cardiovascular diseases. Postprandial glucose was obtained 2 In after a lunch meal. Metabolic syndrome was diagnosed using the criteria Of the U.S. National Cholesterol Education Program Adult Treatment Panel III. Cardiovascular and all-cause deaths were primary outcomes. RESULTS - During a median follow-up of 6.7 years, 410 individuals died, including 82 deaths from cardiovascular causes. In a Cox model adjusting for metabolic syndrome status as well as age, sex, smoking, systolic blood pressure, LDL, and HDL cholesterol levels, elevated 2-h postprandial glucose increased the risk of cardiovascular and all-cause death (per millimole per liter increase, hazard ratio 1.26 [95% CI 1.11-1.42] and 1.10 [1. 04-1.16], respectively), with significant trends across the postprandial glucose quintiles. Including 2-h postprandial glucose into a metabolic syndrome-included mustivariate risk prediction model conferred a discernible improvement of the model in discriminating between those who died of cardiovascular causes and who did not (integrated discrimination improvement 0.4, P = 0. 005; net reclassification improvement 13.4%, P = 0.03); however, the improvement was only marginal for all-cause death. CONCLUSIONS - Given the risk prediction based on metabolic syndrome and established cardiovascular risk factors, 2-h postprandial glucose improves the predictive ability to identity nondiabetic individuals at increased risk of cardiovascular death