scholarly journals Performance of a Framingham cardiovascular risk model among Indians and Europeans in New Zealand and the role of body mass index and social deprivation

Open Heart ◽  
2018 ◽  
Vol 5 (2) ◽  
pp. e000821 ◽  
Author(s):  
Kjersti Stormark Rabanal ◽  
Haakon Eduard Meyer ◽  
Romana Pylypchuk ◽  
Suneela Mehta ◽  
Randi Marie Selmer ◽  
...  
Keyword(s):  
Body Mass Index ◽  
New Zealand ◽  
Body Mass ◽  
Risk Score ◽  
Risk Model ◽  
Framingham Score ◽  
Cvd Risk ◽  
Indian Women ◽  
Framingham Risk ◽  
Indian Men

ObjectivesTo evaluate a Framingham 5-year cardiovascular disease (CVD) risk score in Indians and Europeans in New Zealand, and determine whether body mass index (BMI) and socioeconomic deprivation were independent predictors of CVD risk.MethodsWe included Indians and Europeans, aged 30–74 years without prior CVD undergoing risk assessment in New Zealand primary care during 2002–2015 (n=256 446). Risk profiles included standard Framingham predictors (age, sex, systolic blood pressure, total cholesterol/high-density lipoprotein ratio, smoking and diabetes) and were linked with national CVD hospitalisations and mortality datasets. Discrimination was measured by the area under the receiver operating characteristics curve (AUC) and calibration examined graphically. We used Cox regression to study the impact of BMI and deprivation on the risk of CVD with and without adjustment for the Framingham score.ResultsDuring follow-up, 8105 and 1156 CVD events occurred in Europeans and Indians, respectively. Higher AUCs of 0.76 were found in Indian men (95% CI 0.74 to 0.78) and women (95% CI 0.73 to 0.78) compared with 0.74 (95% CI 0.73 to 0.74) in European men and 0.72 (95% CI 0.71 to 0.73) in European women. Framingham was best calibrated in Indian men, and overestimated risk in Indian women and in Europeans. BMI and deprivation were positively associated with CVD, also after adjustment for the Framingham risk score, although the BMI association was attenuated.ConclusionsThe Framingham risk model performed reasonably well in Indian men, but overestimated risk in Indian women and in Europeans. BMI and socioeconomic deprivation could be useful predictors in addition to a Framingham score.

Usefulness of the Framingham risk score and body mass index to predict early coronary artery calcium in young adults (Muscatine Study)

2001 ◽  
Vol 88 (5) ◽  
pp. 509-515 ◽  
Author(s):  
Larry T Mahoney ◽  
Trudy L Burns ◽  
William Stanford ◽  
Brad H Thompson ◽  
John D Witt ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Young Adults ◽  
Coronary Artery ◽  
Body Mass ◽  
Risk Score ◽  
Coronary Artery Calcium ◽  
Framingham Risk Score ◽  
Framingham Risk

Abstract P020: 1 H Nmr Metabonomic Profiling Identifies Novel Biomarkers for Cardiovascular Mortality

Circulation ◽  
2012 ◽  
Vol 125 (suppl_10) ◽  
Author(s):  
Abtehale Al-Hussaini ◽  
Tsung Tan ◽  
Joban Sehmi ◽  
Paul Elliott ◽  
Mika Ala-Korpela ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Body Mass Index ◽  
Body Mass ◽  
Framingham Risk Score ◽  
Hdl Cholesterol ◽  
Cvd Risk Factors ◽  
Cvd Risk ◽  
H Nmr ◽  
Framingham Risk ◽  
Novel Biomarkers

Background Cardiovascular disease (CVD) is the leading cause of death world-wide. Existing tools for identification of CVD risk have modest predictive power and discrimination. New biomarkers for CVD prediction are urgently needed. Hypothesis We have used 1 H NMR metabonomic profiling of serum to identify novel biomarkers for incident CVD. Methods We investigated 9,179 men and women participating in the London Life Sciences Population study. Participants were recruited between 2002-2008 from the lists of 58 GPs in London UK, and assessed for baseline CVD risk factors, including smoking, body mass index, waist circumference, blood pressure, fasting glucose and lipid profile. Participants were followed for CVD mortality to July 2011 (mean 5.5 years per person). 1 H NMR metabonomic profiling was done on baseline serum sample by 12T Bruker spectrometer, with quantification of 44 fatty acid and low molecular weight markers. Results There were 161 CVD deaths. Compared to survivors, people with CVD death were older, had higher prevalence of type-2 diabetes and cigarette smoking, higher blood pressure, body mass index, glucose, and lower total and HDL cholesterol (Table). Framingham risk scores were higher in cases with CVD death than survivors (18.7±0.9% vs. 11.4±0.7%, P=10 -31 ). We found six NMR metabolites associated with incident CVD at P<0.05; odds ratios (95%CI) for CVD per 1SD increase in biomarker ranged from 1.19 (1.07-1.33, P=0.007) to 1.65 (1.44-1.89, P=3x10 -13 ). The associations of NMR metabolites with CVD were not materially changed by adjustment for age, gender and Framingham risk score. The AUC for prediction of incident CVD was 0.74 using Framingham risk score alone, and 0.77 with incorporation of metabonomic measures. Conclusions We identified six novel biomarkers for incident CVD; these are independent of known CVD risk factors and may improve CVD risk prediction. Our findings demonstrate the potential utility of metabonomic profiling for biomarker discovery and risk stratification. CVD deaths Survivors P Total no of people 161 9018 Age (years) 61.6 (0.5) 53.4 (0.4) 0.001 Male gender (%) 98.2 85.8 <0.001 Smoking (%) 73 49 <0.001 Diabetes (%) 49 20 <0.001 Systolic blood Pressure (mmHg) 144.7 (22.7) 134.0 (19.2) <0.001 Body mass index (kg/m 2 ) 28.4 (5.6) 27.3 (4.3) 0.002 Total cholesterol (mmol/L) 4.8 (1.2) 5.2 (1.1) <0.001 HDL cholesterol (mmol/L) 1.20 1.25 0.064 Glucose (mmol/L) 7.1 5.9 <0.001 Results provided as mean or %.

scholarly journals Connection between severity of sleep disorders, lipid parametres, and antropometric characteristics in patients with hypertension and metabolic syndrom

2020 ◽  
Vol 27 (2) ◽  
pp. 25-33
Author(s):  
G. S. Isayeva ◽  
O. O. Buryakovska
Keyword(s):  
Body Mass Index ◽  
Arterial Pressure ◽  
Sleep Disorders ◽  
Body Mass ◽  
Risk Score ◽  
Epworth Sleepiness Scale ◽  
Framingham Risk Score ◽  
Hdl Cholesterol ◽  
Cardiovascular Risks ◽  
Framingham Risk

The aim – to assess correlations between insomnia, excessive daytime sleepiness (EDS) and levels of lipids, anthropometric parameters and cardiovascular risks in patients with hypertension and metabolic syndrom. Materials and methods. 118 patients with hypertension over 45 years of age were enrolled to this study. The Framingham Risk Score was used to evaluate cardiovascular risks and cardiovascular age. Body mass index, muscular strength, and physical activity (the number of steps per day) were assessed. Total cholesterol, triacylglycerols (TAGs), high-density lipoprotein (HDL) cholesterol, low-density lipoprotein (LDL) cholesterol, glucose and glycated hemoglobin levels were determined. Insomnia was diagnosed in accordance with the International Classification of Sleep Disorders – Third Edition (ICSD-3). EDS was assessed by the Epworth Sleepiness Scale. To detect obstructive sleep apnea, a portable monitoring. Results and discussion. Insomnia was diagnosed in 48 (40.7 %) out of the 118 patients examined. No correlation between insomnia and either metabolic indices or arterial pressure was found. However, levels of systolic arterial pressure, HDL cholesterol, waist circumference, and body mass index were shown to differ depending on the severity of EDS. Analysis of cardiovascular age using the Framingham Risk Score revealed that patients with severe ESD were characterized by a greater cardiovascular age. In group 1 according to the Epworth Sleepiness Scale, it reached 48.0 [45.5–56.7] years, while in groups 2 and 3 this parameter was 57.0 [48.7–63.0] and 72.0 [68.0–80.0] years, respectively (ANOVA test, F=63,4; p=0.001). Conclusions. Thus, evaluation of the impact of sleep disorders on metabolic parameters and arterial hypertension allowed us to reveal that not insomnia itself but EDS as its manifestation is of huge importance. Our findings when using the Epworth Sleepiness Scale suggest that patients with moderate and severe EDS have higher levels of systolic arterial pressure, body mass index, waist circumference, lower HDL cholesterol, and greater cardiovascular age according to the Framingham Risk Score. The presence of insomnia was associated only with low level of high density cholesterol.

scholarly journals T149. CARDIOVASCULAR RISK AND VASCULAR AGE IN ADULTS WITH SCHIZOPHRENIA COMPARED TO A HEALTHY POPULATION: DATA FROM CORTEX-SP STUDY

2020 ◽  
Vol 46 (Supplement_1) ◽  
pp. S287-S288
Author(s):  
Mikel Tous-Espelosin ◽  
Nagore Iriarte-Yoller ◽  
Aitor MartinezAguirre-Betolaza ◽  
Isabel Hervella ◽  
Pablo Corres ◽  
...  
Keyword(s):  
Risk Factors ◽  
Blood Pressure ◽  
Cardiovascular Disease ◽  
Body Mass Index ◽  
Relative Risk ◽  
Body Mass ◽  
Risk Score ◽  
Cvd Risk ◽  
Vascular Age ◽  
Risk Charts

Abstract Background Cardiovascular disease (CVD) is the most common cause of death in people with schizophrenia (SP). The European guidelines on CVD prevention recommend that people with high levels of individual risk factors should automatically have all their risk factors actively managed. It is suggested that CVD risk in SP should be assessed by general risk charts and to include specific relative risk chart for people with severe mental illnesses. Therefore, the purpose of the present study was to estimate CVD risk and vascular age in adults with SP and compared them with a healthy sample. Methods A total of 85 participants with SP (16.2% women, 42.1±10.0 yr old) were compared with 30 HEALTHY participants (60.0% women, 40.0±9.0 yr old). CVD risk was calculated using Systematic Coronary Risk Estimation (SCORE), Framingham Heart Score-Cardiovascular Disease (FRS-CVD), relative risk SCORE and vascular age. Likewise, the variables assessed to calculate the risk charts were age, body mass index, smoking percentage, systolic blood pressure (SBP) through ambulatory blood pressure monitoring during 24 hours and through a fasting biochemical profile, high-density lipoprotein cholesterol (HDL-C) and total cholesterol (TC). Results All HEALTHY variables were in normal values. Sample with SP showed overweight (body mass index=27.1±6.1 kg∙m-2) and higher (P&lt;0.001) smoking percentage than HEALTHY (69.8% vs. 16.1%). Both groups presented normotensive SBP values (SP=115±15 mmHg, HEALTHY=113±10 mmHg). Concerning cholesterol profile, SP showed lower to optimal values in HDL-C (39.0±12.0 mg/dL), yet both were in optimal TC levels (SP=189.7±44 mg/dL, HEALTHY=183.6±35.1 mg/dL). Considering SCORE, both groups were in low risk values with higher (P&lt;0.001) values in SP (0.6±1.0 vs. 0.1±0.4). However, according to relative risk SCORE and FRS-CVD, SP showed medium risk (2.0±1.0; 6.7±12.3), and HEALTHY low (1.0±0.4; 2.6±2.8) risk, respectively. Vascular age was higher (P&lt;0.001) in SP than HEALTHY (48.0±26.0 vs. 36.0±24.0 yr). Discussion Patients suffering from SP compared to HEALTHY showed higher CVD risk and vascular age. These results strongly suggest the promotion of a healthy lifestyle behavior in order to optimize risk factors.

scholarly journals Interaction of Body Mass Index and Framingham Risk Score in Predicting Incident Coronary Disease in Families

Circulation ◽  
2005 ◽  
Vol 111 (15) ◽  
pp. 1871-1876 ◽  
Author(s):  
Samia Mora ◽  
Lisa R. Yanek ◽  
Taryn F. Moy ◽  
M. Daniele Fallin ◽  
Lewis C. Becker ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Coronary Disease ◽  
Body Mass ◽  
Risk Score ◽  
Framingham Risk Score ◽  
Framingham Risk

scholarly journals Interaction of Body Mass Index and Framingham Risk Score in Predicting Incident Coronary Disease in Families

2005 ◽  
Vol 14 (8) ◽  
pp. 11
Author(s):  
S. Mora ◽  
L.R. Yanek ◽  
T.F. Moy ◽  
D. Fallin ◽  
L.C. Becker ◽  
...  
Keyword(s):  
Body Mass Index ◽  
Coronary Disease ◽  
Body Mass ◽  
Risk Score ◽  
Framingham Risk Score ◽  
Framingham Risk

scholarly journals Body Mass Index, Multi-Morbidity, and COVID-19 Risk Factors as Predictors of Severe COVID-19 Outcomes

2021 ◽  
Vol 12 ◽  
pp. 215013272110185
Author(s):  
Sanjeev Nanda ◽  
Audry S. Chacin Suarez ◽  
Loren Toussaint ◽  
Ann Vincent ◽  
Karen M. Fischer ◽  
...  
Keyword(s):  
Risk Factors ◽  
Body Mass Index ◽  
Hospital Admission ◽  
Body Mass ◽  
Risk Score ◽  
The Body ◽  
Risk Scores ◽  
Nasopharyngeal Swab ◽  
Morbidity Score ◽  
The Impact

Purpose The purpose of the present study was to investigate body mass index, multi-morbidity, and COVID-19 Risk Score as predictors of severe COVID-19 outcomes. Patients Patients from this study are from a well-characterized patient cohort collected at Mayo Clinic between January 1, 2020 and May 23, 2020; with confirmed COVID-19 diagnosis defined as a positive result on reverse-transcriptase-polymerase-chain-reaction (RT-PCR) assays from nasopharyngeal swab specimens. Measures Demographic and clinical data were extracted from the electronic medical record. The data included: date of birth, gender, ethnicity, race, marital status, medications (active COVID-19 agents), weight and height (from which the Body Mass Index (BMI) was calculated, history of smoking, and comorbid conditions to calculate the Charlson Comorbidity Index (CCI) and the U.S Department of Health and Human Services (DHHS) multi-morbidity score. An additional COVID-19 Risk Score was also included. Outcomes included hospital admission, ICU admission, and death. Results Cox proportional hazards models were used to determine the impact on mortality or hospital admission. Age, sex, and race (white/Latino, white/non-Latino, other, did not disclose) were adjusted for in the model. Patients with higher COVID-19 Risk Scores had a significantly higher likelihood of being at least admitted to the hospital (HR = 1.80; 95% CI = 1.30, 2.50; P < .001), or experiencing death or inpatient admission (includes ICU admissions) (HR = 1.20; 95% CI = 1.02, 1.42; P = .028). Age was the only statistically significant demographic predictor, but obesity was not a significant predictor of any of the outcomes. Conclusion Age and COVID-19 Risk Scores were significant predictors of severe COVID-19 outcomes. Further work should examine the properties of the COVID-19 Risk Factors Scale.

scholarly journals Association of a Genetic Risk Score With Body Mass Index

JAMA ◽  
2016 ◽  
Vol 316 (17) ◽  
pp. 1825
Author(s):  
Marcus R. Munafò ◽  
Kate Tilling ◽  
George Davey Smith
Keyword(s):  
Body Mass Index ◽  
Body Mass ◽  
Risk Score ◽  
Genetic Risk ◽  
Genetic Risk Score
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