Body Mass Index vs Cholesterol in Cardiovascular Disease Risk Prediction Models

Faeh, David; Braun, Julia; Bopp, Matthias

doi:10.1001/2013.jamainternmed.327

Cited by 20 publications

(16 citation statements)

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“…Overall, the fact that simple anthropometric indices, such as BMI or waist circumference, are associated with long-term CVD incidence in our study cohort population survey showed that compared to cholesterol, the use of BMI in the study models revealed higher CVD risk at all ages, and could better discriminate persons at high and low CVD risk 43 . Moreover, other authors using electronic health records reported that using BMI instead of cholesterol resulted in at least equivalent CVD risk estimation 44 .…”

Section: Discussionmentioning

confidence: 65%

Modeling anthropometric indices in relation to 10-year (2002–2012) incidence of cardiovascular disease, among apparently healthy individuals: The ATTICA study

Filippatos

Kyrou

Georgousopoulou

et al. 2017

Diabetes & Metabolic Syndrome: Clinical Research & Revi

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Section: Discussionmentioning

confidence: 65%

Modeling anthropometric indices in relation to 10-year (2002–2012) incidence of cardiovascular disease, among apparently healthy individuals: The ATTICA study

Filippatos

Kyrou

Georgousopoulou

et al. 2017

Diabetes & Metabolic Syndrome: Clinical Research & Revi

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“…Body size and shape are directly linked to human health: being both underweight or overweight/obese is associated with increased morbidity and mortality risks (Engeland et al, 2003; World Health Organization (WHO), 2009; Faeh, Braun & Bopp, 2012; Flegal et al, 2013; Kit, Ogden & Flegal, 2014). The prevalence of overweight and obesity (OW/OB) is still on the rise worldwide (NCD-RisC, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…The prevalence of overweight and obesity (OW/OB) is still on the rise worldwide (NCD-RisC, 2016). Switzerland is no exception; OW/OB numbers have increased since the 1990s with significant variation by sex, age, region, migration status, and/or socio-economic background (Faeh, Braun & Bopp, 2012). Monitoring OW/OB on the population and/or individual level and identifying groups at risk is essential to plan public health interventions in order to reduce future health and economic burdens at the societal level (Schmid et al, 2005; Schneider et al, 2010; Davin et al, 2012; Schneider & Venetz, 2014).…”

Section: Introductionmentioning

confidence: 99%

Comparison of 3D laser-based photonic scans and manual anthropometric measurements of body size and shape in a validation study of 123 young Swiss men

Koepke

Zwahlen

Wells

et al. 2017

PeerJ

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BackgroundManual anthropometric measurements are time-consuming and challenging to perform within acceptable intra- and inter-individual error margins in large studies. Three-dimensional (3D) laser body scanners provide a fast and precise alternative: within a few seconds the system produces a 3D image of the body topography and calculates some 150 standardised body size measurements.ObjectiveThe aim was to enhance the small number of existing validation studies and compare scan and manual techniques based on five selected measurements. We assessed the agreement between two repeated measurements within the two methods, analysed the direct agreement between the two methods, and explored the differences between the techniques when used in regressions assessing the effect of health related determinants on body shape indices.MethodsWe performed two repeated body scans on 123 volunteering young men using a Vitus Smart XXL body scanner. We manually measured height, waist, hip, buttock, and chest circumferences twice for each participant according to the WHO guidelines. The participants also filled in a basic questionnaire.ResultsMean differences between the two scan measurements were smaller than between the two manual measurements, and precision as well as intra-class correlation coefficients were higher. Both techniques were strongly correlated. When comparing means between both techniques we found significant differences: Height was systematically shorter by 2.1 cm, whereas waist, hip and bust circumference measurements were larger in the scans by 1.17–4.37 cm. In consequence, body shape indices also became larger and the prevalence of overweight was greater when calculated from the scans. Between 4.1% and 7.3% of the probands changed risk category from normal to overweight when classified based on the scans. However, when employing regression analyses the two measurement techniques resulted in very similar coefficients, confidence intervals, and p-values.ConclusionFor performing a large number of measurements in a large group of probands in a short time, body scans generally showed good feasibility, reliability, and validity in comparison to manual measurements. The systematic differences between the methods may result from their technical nature (contact vs. non-contact).

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“…Derived risk prediction models and risk charts include the Framingham Risk Score or, from Europe, scores from PROCAM (Prospective Cardiovascular Münster Heart Study) or SCORE (Systematic COronary Risk Evaluation) [1] [2] [3]. Based on a large population sample from Switzerland with long mortality follow-up, cholesterol parameters contributed only little to prediction of mortality risk [4]. Traditional risk scores have been established decades ago.…”

Section: Introductionmentioning

confidence: 99%

Better risk assessment with glycated hemoglobin instead of cholesterol in CVD risk prediction charts

2013

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Traditional risk charts for the prediction of cardiovascular disease (CVD) include cholesterol parameters. We evaluated how models predict fatal CVD when cholesterol is replaced by glucose parameters. We used data from NHANES III, a US survey conducted 1988-1994 (follow-up until 2006); 15,454 participants (1,716 CVD deaths) were included. Based on the ESC SCORE method, we used age, sex, blood pressure, smoking and either of the following: (1) total cholesterol, (2) total-to-HDLcholesterol, (3) glucose, (4) glycated hemoglobin (A1C). Scaled Brier score (BS), Nagelkerke's R(2) (NR) and integrated discrimination improvement (IDI) were used for model comparison. The ranking (best to worst) was: A1C (BS = 11.62 %; NR = 0.0865; IDI = 0.0091), glucose (11.16 %; 0.0734; 0.0067), total-to-HDL-cholesterol (9.97 %; 0.0547; 0.0010), cholesterol (9.75 %; 0.0484; 0, reference). Differences between models with cholesterol and glucose or A1C were statistically significant. This study suggests the use of A1C instead of cholesterol parameters in charts to assess CVD risk. Abstract Traditional risk charts for the prediction of cardiovascular disease (CVD) include cholesterol parameters. We evaluated how models predict fatal CVD when cholesterol is replaced by glucose parameters.We used data from NHANES III, a US survey conducted 1988-1994 (follow-up until 2006); 15,454 participants (1,716 CVD deaths) were included. Based on the ESC SCORE method, we used age, sex, blood pressure, smoking and either of the following: 1) total cholesterol, 2) total-to-HDL-cholesterol, 3) glucose, 4) glycated hemoglobin (A1C). Scaled Brier score (BS), Nagelkerke's R 2 (NR) and integrated discrimination improvement (IDI) were used for model comparison. The ranking (best to worst) was: A1C (BS=11.62%; NR=0.0865; IDI=0.0091), glucose (11.16%; 0.0734; 0.0067), total-to-HDL-cholesterol (9.97%; 0.0547; 0.0010), cholesterol (9.75%; 0.0484; 0, reference). Differences between models with cholesterol and glucose or A1C were statistically significant. This study suggests the use of A1C instead of cholesterol parameters in charts to assess CVD risk.

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Body Mass Index vs Cholesterol in Cardiovascular Disease Risk Prediction Models

Cited by 20 publications

References 8 publications

Modeling anthropometric indices in relation to 10-year (2002–2012) incidence of cardiovascular disease, among apparently healthy individuals: The ATTICA study

Modeling anthropometric indices in relation to 10-year (2002–2012) incidence of cardiovascular disease, among apparently healthy individuals: The ATTICA study

Comparison of 3D laser-based photonic scans and manual anthropometric measurements of body size and shape in a validation study of 123 young Swiss men

Better risk assessment with glycated hemoglobin instead of cholesterol in CVD risk prediction charts

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