Plasma amyloid-β40 in relation to subclinical atherosclerosis and cardiovascular disease: A population-based study.

BACKGROUND AND AIMS We aimed to determine associations of plasma amyloid-β40 (Aβ40) with subclinical atherosclerosis and risk of atherosclerotic cardiovascular disease (ASCVD) in the general population. METHODS Between 2002 and 2005, plasma Aβ40 was measured by single molecule array (SiMoA®) in 3879 participants of the population-based Rotterdam Study (mean age: 71 years, 61% female). Subclinical atherosclerosis was quantified as computed tomography-assessed calcification volumes. We determined the association of Aβ40 with calcification volumes and clinical ASCVD event risk, and repeated the analyses for ASCVD in a replication cohort of 1467 individuals. RESULTS Higher levels of Aβ40 were associated with increased volumes of calcification in the coronary arteries and to a lesser extent extracranial carotid arteries, independent of traditional cardiovascular risk factors. Of all 3879 participants, 748 developed ASCVD during a median 9.7 years of follow-up. In age- and sex-adjusted models, higher Aβ40 predisposed to a minor increase in ASCVD risk (HR [95%CI]: 1.11[1.02-1.21] per 1-SD increase in Aβ40), driven by coronary heart disease (HR: 1.17[1.05-1.29]) rather than stroke (HR: 1.04[0.93-1.16]). However, excess risk of clinical outcomes was largely explained by baseline differences in cardiovascular risk factors and attenuated after further adjustment (for ASCVD- HR: 1.05[0.96-1.15] and for CHD- HR: 1.08[0.96-1.20]). Results were similar in the replication cohort, with highest risk estimates for CHD (HR: 1.24[1.04-1.48]) in age- and sex-adjusted models, attenuated after adjustment for cardiovascular risk factors (HR: 1.15[0.96-1.39]). CONCLUSIONS In this population-based study, higher plasma amyloid-β40 is associated with subclinical atherosclerosis, but not risk of first-ever ASCVD after accounting for traditional cardiovascular risk factors

Performance of Framingham cardiovascular disease (CVD) predictions in the Rotterdam Study taking into account competing risks and disentangling CVD into coronary heart disease (CHD) and stroke

AbstractBackgroundTo evaluate the performance of Framingham predictions of cardiovascular disease (CVD) risk corrected for the competing risk of non-CVD death, in an independent European cohort of older individuals and subsequently extend the predictions by disentangling CVD into coronary heart disease (CHD) and stroke separately.MethodsWe used the Rotterdam Study data, a prospective cohort study of individuals aged 55years and older (N=6004), to validate the Framingham predictions of CVD, defined as first occurrence of myocardial infarction, coronary death or stroke during 15years of follow-up, corrected for the competing risk of non-CVD death. We subsequently estimated the risks of CHD and stroke separately, and used the sum as a predictor for the total CVD risk. Calibration plots and c-statistics were used to evaluate the performance of the models.ResultsPerformance of the Framingham predictions was good in the low- to intermediate risk (≤30%, 15-year CVD risk) (17.5% observed vs. 16.6% expected) but poorer in the higher risk (>30%) categories (36.3% observed vs. 44.1% expected). The c-statistic increased from 0.66 to 0.69 after refitting. Separately estimating CHD and stroke revealed considerable heterogeneity with regard to the contribution of CHD and stroke to total CVD risk.ConclusionsFramingham CVD risk predictions perform well in the low- to intermediate risk categories in the Rotterdam Study. Disentangling CVD into CHD and stroke separately provides additional information about the individual contribution of CHD and stroke to total individual CVD risk

Development and Validation of a Dementia Risk Prediction Model in the General Population: An Analysis of Three Longitudinal Studies

© 2019 American Psychiatric Association. All rights reserved. Objective: Identification of individuals at high risk of dementia is essential for development of prevention strategies, but reliable tools are lacking for risk stratification in the population. The authors developed and validated a prediction model to calculate the 10-year absolute risk of developing dementia in an aging population. Methods: In a large, prospective population-based cohort, data were collected on demographic, clinical, neuropsychological, genetic, and neuroimaging parameters from 2,710 nondemented individuals age 60 or older, examined between 1995 and 2011. A basic and an extended model were derived to predict 10-year risk of dementia while taking into account competing risks from death due to other causes. Model performance was assessed using optimism-corrected C-statistics and calibration plots, and the models were externally validated in the Dutch population-based Epidemiological Prevention Study of Zoetermeer and in the Alzheimer’s Disease Neuroimaging Initiative cohort 1 (ADNI-1). Results: During a follow-up of 20,324 person-years, 181 participants developed dementia. A basic dementia risk model using age, history of stroke, subjective memory decline, and need for assistance with finances or medication yielded a C-statistic of 0.78 (95% CI=0.75, 0.81). Subsequently, an extended model incorporating the basic model and additional cognitive, genetic, and imaging predictors yielded a C-statistic of 0.86 (95% CI=0.83, 0.88). The models performed well in external validation cohorts from Europe and the United States. Conclusions: In community-dwelling individuals, 10-year dementia risk can be accurately predicted by combining information on readily available predictors in the primary care setting. Dementia prediction can be further improved by using data on cognitive performance, genotyping, and brain imaging. These models can be used to identify individuals at high risk of dementia in the population and are able to inform trial design

Evaluation of newer risk markers for coronary heart disease:The Rotterdam study

Design: Prospective cohort study in the general population of Rotterdam, the Netherlands (The Rotterdam Study). Methods: Data on measurements taken between 1997 and 2001 in 5,933 persons free of CHD (40.6% men; mean age 69.1 years) were collected. We studied the predictive ability of 12 newer risk markers (N-terminal pro-brain natriuretic peptide [NT-proBNP] levels, von Willebrand factor antigen levels, fibrinogen levels, chronic kidney disease, leukocyte count, C-reactive protein levels, homocysteine levels, uric acid levels, coronary artery calcification [CAC] scores obtained by means of CT, carotid intima-media thickness, peripheral arterial disease, and aortic pulse wave velocity). The predictive value was determined by adding a newer marker to a prediction model that was based on traditional cardiovascular risk factors. Results: Risk discrimination improved the most with the addition of CAC scores. A net 23.5% of the individuals who developed CHD were reclassified to a higher risk category, but also 4.2% of those who did not develop CHD. This resulted in a net reclassification improvement (NRI) of 0.193. The CAC score was followed by NT-proBNP (NRI 0.076) in terms of the most improvement to risk classification. Improvements in risk predictions with the other newer markers were marginal. Conclusions: Classification of CHD risk predictions improved most with the addition of the CAC scores to the risk model. Further research is needed to assess whether refinements in risk prediction will actually lead to more effective prevention of cardiovascular disease together with justifiable costs and efforts.</p

Evaluation of newer risk markers for coronary heart disease:The Rotterdam study

Design: Prospective cohort study in the general population of Rotterdam, the Netherlands (The Rotterdam Study). Methods: Data on measurements taken between 1997 and 2001 in 5,933 persons free of CHD (40.6% men; mean age 69.1 years) were collected. We studied the predictive ability of 12 newer risk markers (N-terminal pro-brain natriuretic peptide [NT-proBNP] levels, von Willebrand factor antigen levels, fibrinogen levels, chronic kidney disease, leukocyte count, C-reactive protein levels, homocysteine levels, uric acid levels, coronary artery calcification [CAC] scores obtained by means of CT, carotid intima-media thickness, peripheral arterial disease, and aortic pulse wave velocity). The predictive value was determined by adding a newer marker to a prediction model that was based on traditional cardiovascular risk factors. Results: Risk discrimination improved the most with the addition of CAC scores. A net 23.5% of the individuals who developed CHD were reclassified to a higher risk category, but also 4.2% of those who did not develop CHD. This resulted in a net reclassification improvement (NRI) of 0.193. The CAC score was followed by NT-proBNP (NRI 0.076) in terms of the most improvement to risk classification. Improvements in risk predictions with the other newer markers were marginal. Conclusions: Classification of CHD risk predictions improved most with the addition of the CAC scores to the risk model. Further research is needed to assess whether refinements in risk prediction will actually lead to more effective prevention of cardiovascular disease together with justifiable costs and efforts.</p