Cardiovascular Disease Risk Assessment: Insights from Framingham.
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ABSTRACT:
SUBMITTER: D'Agostino RB
PROVIDER: S-EPMC3673738 | biostudies-other | 2013 Mar
REPOSITORIES: biostudies-other
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Project description:ObjectivesThis study evaluates the risk of cardiovascular disease (CVD) following breast cancer, accounting for baseline CVD risk.MethodsWithin the EPIC-NL (Dutch part of the European Prospective Investigation into Nutrition and Cancer) cohort, 1103 women were diagnosed with breast cancer. For every breast cancer patient, 3-4 women without breast cancer (n = 4328) were selected matched for age, year, and time since cohort enrollment. Based on CVD risk factors at cohort enrollment, 10-year risk of CVD was calculated and categorized: low (< 10%), intermediate (10-20%), high (> 20%). Cox proportional hazard models assessed the risk of CVD events (hospitalization or mortality) and CVD mortality of women with versus without breast cancer, adjusted for baseline CVD risk.ResultsAfter median follow-up of 5 and 6 years, 92 (8.3%) and 325 (7.5%) CVD events occurred in women with and without breast cancer, respectively. In the low CVD risk group, women with breast cancer had 1.44 (95% CI 1.00-2.06) times higher risk of CVD events than women without breast cancer. In the intermediate and high CVD risk categories, risk of CVD events was similar in women with and without breast cancer. Overall, women with breast cancer had 1.77 (95% CI 1.10-2.86) times higher risk of CVD mortality than women without breast cancer.ConclusionsAmong women with low CVD risk, women with breast cancer have a higher risk of CVD event than women without breast cancer. Overall, women with breast cancer have a higher risk of CVD mortality than women without breast cancer.
| S-EPMC5993849 | biostudies-literature
Project description:BackgroundAlthough the inverse association between high-density lipoprotein cholesterol (HDL-C) and risk of cardiovascular disease (CVD) has been long established, it remains unclear whether low HDL-C remains a CVD risk factor when levels of low-density lipoprotein cholesterol (LDL-C) and triglycerides (TG) are not elevated. This is a timely issue because recent studies have questioned whether HDL-C is truly an independent predictor of CVD.Methods and results3590 men and women from the Framingham Heart Study offspring cohort without known CVD were followed between 1987 and 2011. Low HDL-C (<40 mg/dL in men and <50 mg/dL in women) was defined as isolated if TG and LDL-C were both low (<100 mg/dL). We also examined higher thresholds for TG (150 mg/dL) and LDL-C (130 mg/dL) and compared low versus high HDL-C phenotypes using logistic regression analysis to assess association with CVD. Compared with isolated low HDL-C, CVD risks were higher when low HDL-C was accompanied by LDL-C ≥100 mg/dL and TG <100 mg/dL (odds ratio 1.3 [1.0, 1.6]), TG ≥100 mg/dL and LDL-C <100 mg/dL (odds ratio 1.3 [1.1, 1.5]), or TG and LDL-C ≥100 mg/dL (odds ratio 1.6, [1.2, 2.2]), after adjustment for covariates. When low HDL-C was analyzed with higher thresholds for TG (≥150 mg/dL) and LDL-C (≥130 mg/dL), results were essentially the same. In contrast, compared with isolated low HDL-C, high HDL-C was associated with 20% to 40% lower CVD risk except when TG and LDL-C were elevated.ConclusionsCVD risk as a function of HDL-C phenotypes is modulated by other components of the lipid panel.
| S-EPMC4871717 | biostudies-literature
Project description:Present cardiovascular disease (CVD) risk prediction algorithms were developed for a < or =10-year follow up period. Clustering of risk factors at younger ages and increasing life expectancy suggest the need for longer-term risk prediction tools.We prospectively followed 4506 participants (2333 women) of the Framingham Offspring cohort aged 20 to 59 years and free of CVD and cancer at baseline examination in 1971-1974 for the development of "hard" CVD events (coronary death, myocardial infarction, stroke). We used a modified Cox model that allows adjustment for competing risk of noncardiovascular death to construct a prediction algorithm for 30-year risk of hard CVD. Cross-validated survival C statistic and calibration chi2 were used to assess model performance. The 30-year hard CVD event rates adjusted for the competing risk of death were 7.6% for women and 18.3% for men. Standard risk factors (male sex, systolic blood pressure, antihypertensive treatment, total and high-density lipoprotein cholesterol, smoking, and diabetes mellitus), measured at baseline, were significantly related to the incidence of hard CVD and remained significant when updated regularly on follow-up. Body mass index was associated positively with 30-year risk of hard CVD only in models that did not update risk factors. Model performance was excellent as indicated by cross-validated discrimination C=0.803 and calibration chi2=4.25 (P=0.894). In contrast, 30-year risk predictions based on different applications of 10-year functions proved inadequate.Standard risk factors remain strong predictors of hard CVD over extended follow-up. Thirty-year risk prediction functions offer additional risk burden information that complements that of 10-year functions.
| S-EPMC2748236 | biostudies-literature
Project description:The utility of single versus combined blood pressure (BP) components in predicting cardiovascular disease (CVD) events is not established. We compared systolic BP (SBP) and diastolic BP (DBP) versus pulse pressure (PP) and mean arterial pressure (MAP) combined and each of these 4 BP components alone in predicting CVD events.In participants in the original (n=4760) and offspring (n=4897) Framingham Heart Study who were free of CVD events and BP-lowering therapy, 1439 CVD events occurred over serial 4-year intervals from 1952 to 2001. In pooled logistic regression with the use of BP categories, combining SBP with DBP and PP with MAP improved model fit compared with individual BP components (P<0.05 to P<0.0001). Significant interactions were noted between SBP and DBP (P=0.02) and between PP and MAP (P=0.01) in their respective multivariable models. Models with continuous variables for SBP+DBP and PP+MAP proved identical in predicting CVD events (Akaike Information Criteria=10 625 for both). Addition of a quadratic DBP(2) term to DBP and SBP further improved fit (P=0.0016).Combining PP with MAP and SBP with DBP produced models that were superior to single BP components for predicting CVD, and the extent of CVD risk varied with the level of each BP component. The combination of PP+MAP (unlike SBP+DBP) has a monotonic relation with risk and may provide greater insight into hemodynamics of altered arterial stiffness versus impaired peripheral resistance but is not superior to SBP+DBP in predicting CVD events.
| S-EPMC3042701 | biostudies-other
Project description:BackgroundChronic kidney disease (CKD) is generally considered an independent risk factor for cardiovascular disease (CVD) development, but rates in individuals with estimated glomerular filtration rate (eGFR) >60 ml/min/1.73 m(2) are uncertain. The Framingham global CVD risk score (FRS) equation is a widely accepted tool used to predict CVD risk in the general population. The purpose of the present study was to examine whether an association exists between eGFR and FRS in a Chinese population with no CKD or CVD.MethodsA total of 333 participants were divided into three groups based on FRS. The Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation and CKD-EPI equation for Asians (CKD-EPI-ASIA) were used to measure eGFR.ResultsA significant inverse association between eGFR and FRS was confirmed with Pearson correlation coefficients of -0.669, -0.698 (eGFR(CKD-EPI), P<0.01) and -0.658, -0.690 (eGFR(CKD-EPI)-ASIA, P<0.01). This association gradually diminished with progression from the low- to high-risk groups (eGFR(CKD-EPI), r = -0.615, -0.282, -0.197, P<0.01, P<0.01, P>0.05; similar results according to the CKD-EPI-ASIA equation). In the low- or moderate-risk new-groups, this association became stronger with increased FRS (eGFR(CKD-EPI)-ASIA, r = -0557, -0.622 or -0.326, -0.329, P<0.01). In contrast to the results from 2008, eGFR was independently associated with FRS following adjustment for traditional cardiovascular risk factors (P<0.05).ConclusionRenal function has multiple influences on predicting CVD risk in various populations. With increasing FRS and decreasing eGFR, it is also independently associated with CVD, even in individuals with eGFR >60 ml/min/1.73 m(2).
| S-EPMC3896450 | biostudies-literature
Project description:Background In Australia, clinical guidelines for primary prevention of cardiovascular disease recommend the use of the Framingham model to help identify those at high risk of developing the disease. However, this model has not been validated for the Indigenous population. Design Cohort study. Methods Framingham models were applied to the Well Person's Health Check (WPHC) cohort (followed 1998-2014), which included 1448 Aboriginal and Torres Strait Islanders from remote Indigenous communities in Far North Queensland. Cardiovascular disease risk predicted by the original and recalibrated Framingham models were compared with the observed risk in the WPHC cohort. Results The observed five- and 10-year cardiovascular disease probability of the WPHC cohort was 10.0% (95% confidence interval (CI): 8.5-11.7) and 18.7% (95% CI: 16.7-21.0), respectively. The Framingham models significantly underestimated the cardiovascular disease risk for this cohort by around one-third, with a five-year cardiovascular disease risk estimate of 6.8% (95% CI: 6.4-7.2) and 10-year risk estimates of 12.0% (95% CI: 11.4-12.6) and 14.2% (95% CI: 13.5-14.8). The original Framingham models showed good discrimination ability (C-statistic of 0.67) but a significant lack of calibration (χ2 between 82.56 and 134.67). After recalibration the 2008 Framingham model corrected the underestimation and improved the calibration for five-year risk prediction (χ2 of 18.48). Conclusions The original Framingham models significantly underestimate the absolute cardiovascular disease risk for this Australian Indigenous population. The recalibrated 2008 Framingham model shows good performance on predicting five-year cardiovascular disease risk in this population and was used to calculate the first risk chart based on empirical validation using long-term follow-up data from a remote Australian Indigenous population.
| S-EPMC5648047 | biostudies-literature
Project description:AimsChronic kidney disease (CKD) is widely prevalent and independently increases cardiovascular risk. Cardiovascular risk prediction tools derived in the general population perform poorly in CKD. Through large-scale proteomics discovery, this study aimed to create more accurate cardiovascular risk models.Methods and resultsElastic net regression was used to derive a proteomic risk model for incident cardiovascular risk in 2182 participants from the Chronic Renal Insufficiency Cohort. The model was then validated in 485 participants from the Atherosclerosis Risk in Communities cohort. All participants had CKD and no history of cardiovascular disease at study baseline when ∼5000 proteins were measured. The proteomic risk model, which consisted of 32 proteins, was superior to both the 2013 ACC/AHA Pooled Cohort Equation and a modified Pooled Cohort Equation that included estimated glomerular filtrate rate. The Chronic Renal Insufficiency Cohort internal validation set demonstrated annualized receiver operating characteristic area under the curve values from 1 to 10 years ranging between 0.84 and 0.89 for the protein and 0.70 and 0.73 for the clinical models. Similar findings were observed in the Atherosclerosis Risk in Communities validation cohort. For nearly half of the individual proteins independently associated with cardiovascular risk, Mendelian randomization suggested a causal link to cardiovascular events or risk factors. Pathway analyses revealed enrichment of proteins involved in immunologic function, vascular and neuronal development, and hepatic fibrosis.ConclusionIn two sizeable populations with CKD, a proteomic risk model for incident cardiovascular disease surpassed clinical risk models recommended in clinical practice, even after including estimated glomerular filtration rate. New biological insights may prioritize the development of therapeutic strategies for cardiovascular risk reduction in the CKD population.
| S-EPMC10281556 | biostudies-literature
Project description:Genome-wide association studies have been published since 2005 and remain exemplary in translating knowledge fostered by the human genome project into genomic lessons on health and disease. Although our understanding of the basis of complex disease remains by far incomplete, the knowledge of the genetic basis of cardiovascular risk factors and their end organ damage has been significantly improved. The Framingham Heart Study was one of the earliest population-based studies to apply genomic methods and is an important contributor to large disease-based consortia as the International Consortium for Blood Pressure Genome-Wide Association Studies (ICBP), the Global Lipids Genetics Consortium (GLGC), the DIAbetes Genetics Replication And Meta-analysis consortium (DIAGRAM), and the Coronary ARtery DIsease Genome-wide Replication And Meta-Analysis consortium (CARDIoGRAM). The variability of these principal cardiovascular risk factors is to large extent genetic and knowledge on the genetic basis originated largely from analysis of monogenic disease in rare syndromes before the use of genome-wide, common SNP analysis. Genome-wide association studies have identified ~45 common variants associated with systolic- and diastolic blood pressure, ~65 common variants for type 2 diabetes and ~95 common variants for lipid traits. One major end organ damage is coronary heart disease and ~25 loci could be shown to be associated. Risk scores using multiple cardiovascular risk factor SNPs are clearly correlated with cardiovascular outcome. This review summarizes recent findings by genome-wide association studies and the contributions by the Framingham Heart Study on the basis of seminal articles and gives an outlook on some of the future experiments.
| S-EPMC3708670 | biostudies-other
Project description:Background The American Heart Association's framework "ideal cardiovascular health" (CVH) focuses on modifiable risk factors to reduce cardiovascular disease (CVD). Metabolomics provides important pathobiological insights into risk factors and CVD development. We hypothesized that metabolomic signatures associate with CVH status, and that metabolites, at least partially, mediate the association of CVH score with atrial fibrillation (AF) and heart failure (HF). Methods and Results We studied 3056 adults in the FHS (Framingham Heart Study) cohort to evaluate CVH score and incident outcomes of AF and HF. Metabolomics data were available in 2059 participants; mediation analysis was performed to evaluate the mediation of metabolites in the association of CVH score and incident AF and HF. In the smaller cohort (mean age, 54 years; 53% women), CVH score was associated with 144 metabolites, with 64 metabolites shared across key cardiometabolic components (body mass index, blood pressure, and fasting blood glucose) of the CVH score. In mediation analyses, 3 metabolites (glycerol, cholesterol ester 16:1, and phosphatidylcholine 32:1) mediated the association of CVH score with incident AF. Seven metabolites (glycerol, isocitrate, asparagine, glutamine, indole-3-proprionate, phosphatidylcholine C36:4, and lysophosphatidylcholine 18:2), partly mediated the association between CVH score and incident HF in multivariable-adjusted models. Conclusions Most metabolites that associated with CVH score were shared the most among 3 cardiometabolic components. Three main pathways: (1) alanine, glutamine, and glutamate metabolism; (2) citric acid cycle metabolism; and (3) glycerolipid metabolism mediated CVH score with HF. Metabolomics provides insights into how ideal CVH status contributes to the development of AF and HF.
| S-EPMC10356055 | biostudies-literature
Project description:BACKGROUND:In the elderly, impaired cognition may weaken medication adherence and compromise treatment for cardiovascular disease (CVD). AIM:We examined risk factors for medication adherence and the relationship between adherence and levels of CVD risk factors among older participants with hypertension, dyslipidaemia and diabetes in the Framingham Heart Study. METHODS:The four-item Morisky Medication Adherence Scale was administered to 1559 participants, median age 70 years, 53% women. We created an adherence score, ranging from 0 to 4, with low adherence defined as a score ?2. CVD risk factors were assessed using standard protocols. Cognition was measured using the Mini-Mental State Examination (MMSE) and depressive symptoms were measured using the Center for Epidemiologic Studies of Depression (CES-D) scale. RESULTS:Among participants who self-reported taking antihypertensive, lipid-lowering and/or hyperglycaemic medication(s), 12% (n = 191) had low medication adherence. The risk of low adherence increased by 45% (95% confidence interval (CI): 25-68%, P < 0.001) per five-unit increase in CES-D score. In participants taking antihypertensive medication (n = 1017), low adherence was associated with higher mean diastolic blood pressure (73 mmHg, 95% CI: 71-75 vs 71 mmHg, 95% CI: 70-71; P = 0.04) after adjusting for covariates. Among participants taking lipid-lowering medication (n = 937), low adherence was associated with higher mean low-density lipoprotein cholesterol (92 mg/dL, 95% CI: 87-96 vs 86 mg/dL, 95% CI: 84-88; P = 0.03). Low adherence was not associated with fasting plasma glucose (P = 0.10) or haemoglobin A1c (P = 0.68) in the subgroup of participants (n = 192) taking hypoglycaemic medication. CONCLUSIONS:Depressive symptoms might act as a barrier for medication adherence, which exacerbates CVD risk factors in older-aged adults.
| S-EPMC5889324 | biostudies-literature