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Fuster-Parra P.,University of the Balearic Islands | Fuster-Parra P.,University Institute of Health Sciences | Tauler P.,University Institute of Health Sciences | Bennasar-Veny M.,University Institute of Health Sciences | And 3 more authors.
Computer Methods and Programs in Biomedicine | Year: 2016

An extensive, in-depth study of cardiovascular risk factors (CVRF) seems to be of crucial importance in the research of cardiovascular disease (CVD) in order to prevent (or reduce) the chance of developing or dying from CVD. The main focus of data analysis is on the use of models able to discover and understand the relationships between different CVRF. In this paper a report on applying Bayesian network (BN) modeling to discover the relationships among thirteen relevant epidemiological features of heart age domain in order to analyze cardiovascular lost years (CVLY), cardiovascular risk score (CVRS), and metabolic syndrome (MetS) is presented. Furthermore, the induced BN was used to make inference taking into account three reasoning patterns: causal reasoning, evidential reasoning, and intercausal reasoning. Application of BN tools has led to discovery of several direct and indirect relationships between different CVRF. The BN analysis showed several interesting results, among them: CVLY was highly influenced by smoking being the group of men the one with highest risk in CVLY; MetS was highly influence by physical activity (PA) being again the group of men the one with highest risk in MetS, and smoking did not show any influence. BNs produce an intuitive, transparent, graphical representation of the relationships between different CVRF. The ability of BNs to predict new scenarios when hypothetical information is introduced makes BN modeling an Artificial Intelligence (AI) tool of special interest in epidemiological studies. As CVD is multifactorial the use of BNs seems to be an adequate modeling tool. © 2015 Elsevier Ireland Ltd.


Fuster-Parra P.,University of the Balearic Islands | Fuster-Parra P.,University Institute of Health Sciences | Bennasar-Veny M.,University Institute of Health Sciences | Tauler P.,University Institute of Health Sciences | And 3 more authors.
PLoS ONE | Year: 2015

Background: Because the accurate measure of body fat (BF) is difficult, several prediction equations have been proposed. The aim of this study was to compare different multiple regression models to predict BF, including the recently reported CUN-BAE equation. Methods: Multi regression models using body mass index (BMI) and body adiposity index (BAI) as predictors of BF will be compared. These models will be also compared with the CUN-BAE equation. For all the analysis a sample including all the participants and another one including only the overweight and obese subjects will be considered. The BF reference measure was made using Bioelectrical Impedance Analysis. Results: The simplest models including only BMI or BAI as independent variables showed that BAI is a better predictor of BF. However, adding the variable sex to both models made BMI a better predictor than the BAI. For both the whole group of participants and the group of overweight and obese participants, using simple models (BMI, age and sex as variables) allowed obtaining similar correlations with BF as when the more complex CUN-BAE was used (ρ = 0:87 vs. ρ = 0:86 for the whole sample and ρ = 0:88 vs. ρ = 0:89 for overweight and obese subjects, being the second value the one for CUN-BAE). Conclusions: There are simpler models than CUN-BAE equation that fits BF as well as CUN-BAE does. Therefore, it could be considered that CUN-BAE overfits. Using a simple linear regression model, the BAI, as the only variable, predicts BF better than BMI. However, when the sex variable is introduced, BMI becomes the indicator of choice to predict BF. © 2015 Fuster-Parra et al.


Tauler P.,Health Science University | Bennasar-Veny M.,University of the Balearic Islands | Morales-Asencio J.M.,University of the Balearic Islands | Lopez-Gonzalez A.A.,Prevention of Occupational Risks in Health Services | And 5 more authors.
PLoS ONE | Year: 2014

Background: Metabolic Syndrome (MetS) is a complex disorder defined as a cluster of interconnected risk factors such as hypertension, dyslipidemia, obesity and high blood glucose levels. Premorbid metabolic syndrome (PMetS) is defined by excluding patients with previously diagnosed cardiovascular disease or diabetes mellitus from those suffering MetS. We aimed to determine the prevalence of PMetS in a working population, and to analyse the relationship between the diagnostic criteria of the International Diabetes Federation (IDF) and of the National Cholesterol Education Program Adult Treatment Panel III (ATPIII). The relationship between the presence of PMetS and cardiovascular risk factors was also analysed. Research Methodology/Findings: A cross-sectional study was conducted in 24,529 male and 18,736 female Spanish (white western European) adult workers (20-65 years) randomly selected during their work health periodic examinations. Anthropometrics, blood pressure and serum parameters were measured. The presence of MetS and PMetS was ascertained using ATPIII and IDF criteria. Cardiovascular risk was determined using the Framingham-REGICOR equation. The results showed MetS had an adjusted global prevalence of 12.39% using ATPIII criteria and 16.46% using IDF criteria. The prevalence of PMetS was slightly lower (11.21% using ATPIII criteria and 14.72% using IDF criteria). Prevalence in males was always higher than in females. Participants with PMetS displayed higher values of BMI, waist circumference, blood pressure, glucose and triglycerides, and lower HDL-cholesterol levels. Logistic regression models reported lower PMetS risk for females, non-obese subjects, non-smokers and younger participants. Cardiovascular risk determined with Framingham-REGICOR was higher in participants with PMetS. Conclusions: PMetS could be a reliable tool for the early identification of apparently healthy individuals who have a significant risk for developing cardiovascular events and type 2 diabetes. © 2014 Tauler et al.


Lopez A.A.,Prevention of Occupational Risks in Health Services | Cespedes M.L.,Prevention of Occupational Risks in Health Services | Vicente T.,Prevention of Occupational Risks | Tomas M.,University of the Balearic Islands | And 3 more authors.
PLoS ONE | Year: 2012

Background: Body fat content and fat distribution or adiposity are indicators of health risk. Several techniques have been developed and used for assessing and/or determining body fat or adiposity. Recently, the Body Adiposity Index (BAI), which is based on the measurements of hip circumference and height, has been suggested as a new index of adiposity. The aim of the study was to compare BAI and BMI measurements in a Caucasian population from a European Mediterranean area and to assess the usefulness of the BAI in men and women separately. Research Methodology/Principal Findings: A descriptive cross-sectional study was conducted in a Caucasian population. All participants in the study (1,726 women and 1,474 men, mean age 39.2 years, SD 10.8) were from Mallorca (Spain). Anthropometric data, including percentage of body fat mass obtained by Bioelectrical Impedance Analysis, were determined. Body Mass Index (BMI) and BAI were calculated. BAI and BMI showed a good correlation (r = 0.64, p<0.001). A strong correlation was also found between BAI and the % fat determined using BIA (r = 0.74, p<0.001), which is even stronger than the one between BMI and % fat (r = 0.54, p<0.001). However, the ROC curve analysis showed a higher accuracy for BMI than for the BAI regarding the discriminatory capacity. Conclusion: The BAI could be a good tool to measure adiposity due, at least in part, to the advantages over other more complex mechanical or electrical systems. Probably, the most important advantage of BAI over BMI is that weight is not needed. However, in general it seems that the BAI does not overcome the limitations of BMI. © 2012 López et al.


Bennasar-Veny M.,University of the Balearic Islands | Lopez-Gonzalez A.A.,Prevention of Occupational Risks in Health Services | Tauler P.,Health Science University | Cespedes M.L.,Prevention of Occupational Risks in Health Services | And 4 more authors.
PLoS ONE | Year: 2013

Background:Several studies have shown a relation between the adipose tissue accumulation and a higher risk for developing metabolic and cardiovascular diseases. Thus, body fat content and, mainly, the fat distribution or adiposity could be considered as important indicators of health risk. In spite of presenting several limitations, BMI is the most widely used and accepted index for classifying overweight and obesity. The aim of the study was to evaluate the correlations between Body Adiposity Index (BAI), BMI and other adiposity indexes such as WC, WHR and WHtR with cardiovascular and metabolic risk factors. Furthermore, the behavior of BAI and BMI regarding the ability to discriminate overweight or obese individuals was also analyzed.Research Methodology/Principal Findings:A cross-sectional study was conducted in Spanish Caucasian adult workers. Participants in the study (29.214 men and 21.040 women, aged 20-68 years) were systematically selected during their work health periodic examinations. BAI, BMI, WHR, WHtR, body weight, hip and waist circumference (WC) as well as systolic and diastolic blood pressure were measured. Serum levels of high density lipoprotein cholesterol (HDL-C), low density lipoprotein cholesterol (LDL-C), triglycerides (TG) and glucose were also determined. Results of the study indicated that BAI was less correlated with cardiovascular risk factors and metabolic risk factors than other adiposity indexes (BMI, WC and WHtR). The best correlations were found for WHtR. In addition, the BAI presented lower discriminatory capacity than BMI for diagnosing metabolic syndrome (MS) using both IDF and ATP III criteria. A different behavior of the BAI in men and women when considering the ability to discriminate overweight or obese individuals was also observed.Conclusions:The adiposity indexes that include the waist circumference (WHtR and WC) may be better candidates than BAI and BMI to evaluate metabolic and cardiovascular risk in both clinical practice and research. © 2013 Bennasar-Veny et al.

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