Predictive Capacity and Cutoff Value of Waist-to-Height Ratio in the Incidence of Metabolic Syndrome.

The purpose of this study was to compare the predictive ability of waist-to-height ratio (WHtR) compared with other anthropometric indicators in the incidence of metabolic syndrome (MetS) and to propose cutoff values for its early detection in nursing practice. A longitudinal cohort study was conducted on a sample of 630 workers (137 exposed and 493 nonexposed), free of MetS at baseline. WHtR was compared with body mass index (BMI), waist circumference (WC), and the percentage of body fat (BF%). In the Cox regression, the adjusted values of hazard ratio (HR) were 5.4 (confidence interval [CI] = [3.1, 9.5]) for WHtR and 7.4 (CI = [3.7, 14.9]) for components of MetS. WHtR obtained the largest area under the curve 0.82 (CI = [0.76, 0.88]), and with a cutoff value of 0.54, values were obtained for sensitivity (70%) and specificity (77%). WHtR was the best predictor of incidence of MetS, with a cutoff value of 0.54. Nursing can improve the early detection of MetS by measuring WHtR.

Correction: Influence of Obesity and Metabolic Disease on Carotid Atherosclerosis in Patients with Coronary Artery Disease (CordioPrev Study).

[This corrects the article DOI: 10.1371/journal.pone.0153096.].

Influence of Obesity and Metabolic Disease on Carotid Atherosclerosis in Patients with Coronary Artery Disease (CordioPrev Study).

BACKGROUND: Recent data suggest that the presence of associated metabolic abnormalities may be important modifiers of the association of obesity with a poorer prognosis in coronary heart disease. We determined the influence of isolated overweight and obesity on carotid intima media thickness (IMT-CC), and also assessed whether this influence was determined by the presence of metabolic abnormalities. METHODS: 1002 participants from the CordioPrev study were studied at entry. We determined their metabolic phenotypes and performed carotid ultrasound assessment. We evaluated the influence of obesity, overweight and metabolic phenotypes on the IMT-CC. RESULTS: Metabolically sick participants (defined by the presence of two or more metabolic abnormalities) showed a greater IMT-CC than metabolically healthy individuals (p = 4 * 10(-6)). Overweight and normal weight patients who were metabolically healthy showed a lower IMT-CC than the metabolically abnormal groups (all p<0.05). When we evaluated only body weight (without considering metabolic phenotypes), overweight or obese patients did not differ significantly from normal-weight patients in their IMT-CC (p = 0.077). However, obesity was a determinant of IMT-CC when compared to the composite group of normal weight and overweight patients (all not obese). CONCLUSIONS: In coronary patients, a metabolically abnormal phenotype is associated with a greater IMT-CC, and may be linked to a higher risk of suffering new cardiovascular events. The protection conferred in the IMT-CC by the absence of metabolic abnormality may be blunted by the presence of obesity. TRIAL REGISTRATION: ClinicalTrials.gov NCT00924937.

New non-invasive method for early detection of metabolic syndrome in the working population.

BACKGROUND: We propose a new method for the early detection of metabolic syndrome in the working population, which was free of biomarkers (non-invasive) and based on anthropometric variables, and to validate it in a new working population. METHODS: Prevalence studies and diagnostic test accuracy to determine the anthropometric variables associated with metabolic syndrome, as well as the screening validity of the new method proposed, were carried out between 2013 and 2015 on 636 and 550 workers, respectively. The anthropometric variables analysed were: blood pressure, body mass index, waist circumference, waist-height ratio, body fat percentage and waist-hip ratio. We performed a multivariate logistic regression analysis and obtained receiver operating curves to determine the predictive ability of the variables. The new method for the early detection of metabolic syndrome we present is based on a decision tree using chi-squared automatic interaction detection methodology. RESULTS: The overall prevalence of metabolic syndrome was 14.9%. The area under the curve for waist-height ratio and waist circumference was 0.91 and 0.90, respectively. The anthropometric variables associated with metabolic syndrome in the adjusted model were waist-height ratio, body mass index, blood pressure and body fat percentage. The decision tree was configured from the waist-height ratio (⩾0.55) and hypertension (blood pressure ⩾128/85 mmHg), with a sensitivity of 91.6% and a specificity of 95.7% obtained. CONCLUSIONS: The early detection of metabolic syndrome in a healthy population is possible through non-invasive methods, based on anthropometric indicators such as waist-height ratio and blood pressure. This method has a high degree of predictive validity and its use can be recommended in any healthcare context.

Metabolic phenotypes of obesity influence triglyceride and inflammation homoeostasis.

BACKGROUND: We examined the degree of postprandial triglyceride (TG) response over the day, representing a highly dynamic state, with continuous metabolic adaptations, among normal-weight, overweight and obese patients, according to their metabolically healthy or abnormal status. MATERIALS AND METHODS: A total of 1002 patients from the CORDIOPREV clinical trial (NCT00924937) were submitted to an oral fat load test meal with 0·7 g fat/kg body weight (12% saturated fatty acids (SFA), 10% polyunsaturated fatty acids (PUFA), 43% monounsaturated fatty acids (MUFA), 10% protein and 25% carbohydrates). Serial blood test analysing lipid fractions and inflammation markers (high-sensitivity C-reactive protein (hs-CRP)) were drawn at 0, 1, 2, 3 and 4 h during postprandial state. We explored the dynamic response according to six body size phenotypes: (i) normal weight, metabolically healthy; (ii) normal weight, metabolically abnormal; (iii) overweight, metabolically healthy; (iv) overweight, metabolically abnormal; (v) obese, metabolically healthy; and (vi) obese, metabolically abnormal. RESULTS: Metabolically healthy patients displayed lower postprandial response of plasma TG and large triacylglycerol-rich lipoproteins (TRLs)-TG, compared with those metabolically abnormal, independently whether or not they were obese (P < 0·001 and P < 0·001, respectively). Moreover, the area under the curve (AUC) of TG and AUC of large TRLs-TG were greater in the group of metabolically abnormal compared with the group of metabolically healthy (P < 0·001 and P < 0·001, respectively). Interestingly, metabolically abnormal subjects displayed higher postprandial response of plasma hs-CRP than did the subgroup of normal, overweight and obese, metabolically healthy patients (P < 0·001). CONCLUSIONS: Our findings showed that certain types of the metabolic phenotypes of obesity are more favourable modulating phenotypic flexibility after a dynamic fat load test, through TG metabolism and inflammation homoeostasis. To identify, these phenotypes may be the best strategy for personalized treatment of obesity.