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      Validation of Surrogate Anthropometric Indices in Older Adults: What Is the Best Indicator of High Cardiometabolic Risk Factor Clustering?

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          Abstract

          The present study evaluated the ability of five obesity-related parameters, including a body shape index (ABSI), conicity index (CI), body roundness index (BRI), body mass index (BMI), and waist-to-height ratio (WtHR) for predicting increased cardiometabolic risk in a population of elderly Colombians. A cross-sectional study was conducted on 1502 participants (60.3% women, mean age 70 ± 7.6 years) and subjects’ weight, height, waist circumference, serum lipid indices, blood pressure, and fasting plasma glucose were measured. A cardiometabolic risk index (CMRI) was calculated using the participants’ systolic and diastolic blood pressure, triglycerides, high-density lipoprotein and fasting glucose levels, and waist circumference. Following the International Diabetes Federation definition, metabolic syndrome was defined as having three or more metabolic abnormalities. All surrogate anthropometric indices correlated significantly with CMRI ( p < 0.01). Receiver operating characteristic curve analysis of how well the anthropometric indices identified high cardiometabolic risk showed that WtHR and BRI were the most accurate indices. The best WtHR and BRI cut-off points in men were 0.56 (area under curve, AUC 0.77) and 4.71 (AUC 0.77), respectively. For women, the WtHR and BRI cut-off points were 0.63 (AUC 0.77) and 6.20 (AUC 0.77), respectively. In conclusion, BRI and WtHR have a moderate discriminating power for detecting high cardiometabolic risk in older Colombian adults, supporting the idea that both anthropometric indices are useful screening tools for use in the elderly.

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          A New Body Shape Index Predicts Mortality Hazard Independently of Body Mass Index

          Background Obesity, typically quantified in terms of Body Mass Index (BMI) exceeding threshold values, is considered a leading cause of premature death worldwide. For given body size (BMI), it is recognized that risk is also affected by body shape, particularly as a marker of abdominal fat deposits. Waist circumference (WC) is used as a risk indicator supplementary to BMI, but the high correlation of WC with BMI makes it hard to isolate the added value of WC. Methods and Findings We considered a USA population sample of 14,105 non-pregnant adults ( ) from the National Health and Nutrition Examination Survey (NHANES) 1999–2004 with follow-up for mortality averaging 5 yr (828 deaths). We developed A Body Shape Index (ABSI) based on WC adjusted for height and weight: ABSI had little correlation with height, weight, or BMI. Death rates increased approximately exponentially with above average baseline ABSI (overall regression coefficient of per standard deviation of ABSI [95% confidence interval: – ]), whereas elevated death rates were found for both high and low values of BMI and WC. ( – ) of the population mortality hazard was attributable to high ABSI, compared to ( – ) for BMI and ( – ) for WC. The association of death rate with ABSI held even when adjusted for other known risk factors including smoking, diabetes, blood pressure, and serum cholesterol. ABSI correlation with mortality hazard held across the range of age, sex, and BMI, and for both white and black ethnicities (but not for Mexican ethnicity), and was not weakened by excluding deaths from the first 3 yr of follow-up. Conclusions Body shape, as measured by ABSI, appears to be a substantial risk factor for premature mortality in the general population derivable from basic clinical measurements. ABSI expresses the excess risk from high WC in a convenient form that is complementary to BMI and to other known risk factors.
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            Statistics review 13: Receiver operating characteristic curves

            This review introduces some commonly used methods for assessing the performance of a diagnostic test. The sensitivity, specificity and likelihood ratio of a test are discussed. The uses of the receiver operating characteristic curve and the area under the curve are explained.
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              Cardiovascular consequences of metabolic syndrome.

              The metabolic syndrome (MetS) is defined as the concurrence of obesity-associated cardiovascular risk factors including abdominal obesity, impaired glucose tolerance, hypertriglyceridemia, decreased HDL cholesterol, and/or hypertension. Earlier conceptualizations of the MetS focused on insulin resistance as a core feature, and it is clearly coincident with the above list of features. Each component of the MetS is an independent risk factor for cardiovascular disease and the combination of these risk factors elevates rates and severity of cardiovascular disease, related to a spectrum of cardiovascular conditions including microvascular dysfunction, coronary atherosclerosis and calcification, cardiac dysfunction, myocardial infarction, and heart failure. While advances in understanding the etiology and consequences of this complex disorder have been made, the underlying pathophysiological mechanisms remain incompletely understood, and it is unclear how these concurrent risk factors conspire to produce the variety of obesity-associated adverse cardiovascular diseases. In this review, we highlight current knowledge regarding the pathophysiological consequences of obesity and the MetS on cardiovascular function and disease, including considerations of potential physiological and molecular mechanisms that may contribute to these adverse outcomes.
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                Author and article information

                Journal
                Nutrients
                Nutrients
                nutrients
                Nutrients
                MDPI
                2072-6643
                24 July 2019
                August 2019
                : 11
                : 8
                : 1701
                Affiliations
                [1 ]Department of Health Sciences, Public University of Navarra, Navarrabiomed-Biomedical Research Centre, IDISNA-Navarra’s Health Research Institute, C/irunlarrea 3, Complejo Hospitalario de Navarra, 31008 Pamplona, Navarra, Spain
                [2 ]Faculty of Sport Sciences, University of Huelva, Avenida de las Fuerzas Armadas s/n, 21007 Huelva, Spain
                [3 ]Centro de Investigación Biomédica en Red de Fragilidad y Envejecimiento Saludable (CIBERFES), Instituto de Salud Carlos III, 28029 Madrid, Spain
                [4 ]Hospital Universitario San Ignacio – Aging Institute, Pontificia Universidad Javeriana, Bogotá 110111, Colombia
                [5 ]Department of Nursing, Faculty of Health Sciences, University of Granada, Av. Ilustración, 60, 18016 Granada, Spain
                [6 ]Grupo de Ejercicio Físico y Deportes, Vicerrectoría de Investigaciones, Facultad de Salud, Universidad Manuela Beltrán, Bogotá 110231, DC, Colombia
                Author notes
                [* ]Correspondence: robin640@ 123456hotmail.com ; Tel.: +34-699-993-920
                Author information
                https://orcid.org/0000-0003-3075-6960
                https://orcid.org/0000-0003-4392-6850
                https://orcid.org/0000-0002-1506-4272
                https://orcid.org/0000-0001-5680-7880
                https://orcid.org/0000-0001-5103-6028
                https://orcid.org/0000-0003-2775-0058
                https://orcid.org/0000-0001-9165-4349
                Article
                nutrients-11-01701
                10.3390/nu11081701
                6723899
                31344803
                dd33c9f3-92f8-4d27-8258-7011048f7d29
                © 2019 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 05 July 2019
                : 22 July 2019
                Categories
                Article

                Nutrition & Dietetics
                anthropometric indices,diagnosis criteria,metabolic syndrome,cardiometabolic risk,elderly

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