Comparison of abdominal obesity measures in predicting of 10-year cardiovascular risk in an Iranian adult population using ACC/AHA risk model: A population based cross sectional study

Novel Anthropometric Indices as Screening Tools for Obesity: A Study on Healthy Iranians

Background and Aims

Upper body fat distribution is more related to cardiometabolic diseases than central obesity. Neck circumference (NC) and neck-to-height ratio (NHtR) are two indicators of upper body obesity that are affordable, easy to obtain, highly reproducible, and more practical in the crowded health centers than the classic anthropometric indices.

Methods

18-65-year-old individuals with no past medical history were included. After obtaining written informed consent, they were screened for hypertension, high blood glucose, and other abnormal laboratory results. Data were analyzed using SPSS and Mann-Whitney U test, Chi square test, Spearman's correlation coefficient, and ROC curve.

Results

In our 2,812 participants, NC had the lowest area under the curve (AUC) in both male and female obese and overweight subjects. NHtR and hip circumference (HC) had the highest AUC in men and women with obesity, respectively. The highest sensitivity for overweight men and women belonged to waist circumference (WC) and waist-to-height ratio (WHtR), respectively, and for both males and females with obesity, NHtR had the highest sensitivity. The cutoff point of NHtR had the same value for males and females. HC and NHtR had the highest positive likelihood ratio (PLR) for obesity in men. In addition, HC and WC had the highest PLR for obesity in women.

Conclusion

In this study, we revealed that NC had the lowest and NHtR and HC had the highest predictive value for obesity. Furthermore, for both males and females with obesity, NHtR had the highest sensitivity. HC had the highest PLR for obesity in both genders. Our results warrant prospective studies to evaluate the role of NHtR and other novel anthropometric indices in the risk of cardiometabolic diseases.

A New Approach Using BMI and FMI as Predictors of Cardio-Vascular Risk Factors among Mexican Young Adults

The study aimed to identify accurate cut-off points for waist circumference (WC), body fat percentage (BF%), body mass index (BMI), fat mass index (FMI), and fat-free mass index (FFMI), and to determine their effective accuracy to predict cardiovascular risk factors (CVRFs) among Mexican young adults. A cross-sectional study was conducted among 1730 Mexican young adults. Adiposity measures and CVRFs were assessed under fasting conditions. The optimal cut-off points were assessed using the receiver operating characteristic curve (ROC). Age-adjusted odds ratios (OR) were used to assess the associations between anthropometric measurements and CVRFs. The cut-off values found, in females and males, respectively, for high WC (≥72.3 and ≥84.9), high BF% (≥30 and ≥22.6), high BMI (≥23.7 and ≥24.4), high FMI (≥7.1 and ≥5.5), and low FFMI (≤16 and ≤18.9) differ from those set by current guidelines. High BMI in women, and high FMI in men, assessed by the 50th percentile, had the best discriminatory power in detecting CVRFs, especially high triglycerides (OR: 3.07, CI: 2.21-4.27 and OR: 3.05, CI: 2.28-4.08, respectively). Therefore, these results suggest that BMI and FMI measures should be used to improve the screening of CVRFs in Mexican young adults.

Cut points of the conicity index as an indicator of abdominal obesity in individuals undergoing hemodialysis: An analysis of latent classes

OBJECTIVES

Abdominal obesity favors the involvement of cardiometabolic complications in renal patients on hemodialysis. Thus, the aim of the study was to identify the cut-points of the conicity index in individuals undergoing hemodialysis.

METHODS

This was a cross-sectional study carried out with 953 individuals undergoing hemodialysis in clinics in a metropolitan region of southeastern Brazil. The conicity index was calculated using the following mathematical equation: waist circumference/0.109 × √weight/height. The receiver operating characteristic (ROC) curve was calculated from the analysis of latent classes by cross-validation through a latent variable of abdominal obesity. This latent variable was defined using the response pattern of the observed anthropometric variables considering the presence and absence of abdominal obesity: waist circumference, waist-to-height ratio, and body shape index. The cut-points identified were elucidated by the area under the curve (AUC), Youden index, sensitivity, and specificity.

RESULTS

The cut-points for the conicity index found for both sexes were similar, resulting in a cut-point for men of 1.275 (AUC, 0.921; Youden index, 0.666), with a sensitivity and specificity of 83% and 83.6%, and a cut-point for women of 1.285 (AUC, 0.921; Youden index, 0.679), with a sensitivity and specificity of 78.6% and 89.3%, respectively.

CONCLUSIONS

The conicity index showed high discriminatory power for the identification of abdominal obesity in hemodialysis patients, therefore it can be a simple and easily accessible tool to be incorporated into clinical practice in this population.

Identification of the Best Anthropometric Index for Predicting the 10-Year Cardiovascular Disease in Southwest China: A Large Single-Center, Cross-Sectional Study

INTRODUCTION

This population-based cross-sectional study aimed to identify the best predictor of the 10-year cardiovascular (CV) high risk among old and new anthropometric indices.

METHODS

We investigated 76,915 adults older than 18 years of age living in southwest China. Ten obesity indices were calculated. The 10-year cardiovascular disease (CVD) risk was estimated using the Framingham risk score. Receiver operating characteristic curve analysis was performed to assess the ability of the anthropometric index to predict the 10-year high risk of CVD events.

RESULTS

The waist-to-hip ratio (WHR) had the highest area under the curve (AUC) value (0.711; sensitivity: 62.22%, specificity: 42.73%) in men, while the body fat index (BAI) had the lowest AUC value (0.624, sensitivity: 49.07%, specificity: 54.84%). The waist-to-height ratio (WHtR) and the body roundness index (BRI) showed the highest AUC value (0.751, sensitivity: 39.24%, 39.83%, specificity: 75.68%, 68.59%) in women, while the BAI showed the lowest AUC value (0.671, sensitivity: 53.15%, specificity: 57.14%).

CONCLUSIONS

The WHR was the best anthropometric measure for assessing the 10-year high risk of CVD in men, while the WHtR and BRI were the best measures for women. In men, the WHR should be < 0.88, and in women, the WHtR should be < 0.502 or the BRI should be < 3.41.

Comparison of laboratory-based and non-laboratory-based WHO cardiovascular disease risk charts: a population-based study

Background

Determining the risk of Cardiovascular Disease (CVD) is a necessity for timely preventive interventions in high-risk groups. However, laboratory testing may be impractical in countries with limited resources. This study aimed at comparison and assessment of the agreement between laboratory-based and non-laboratory-based WHO risk charts models.

Methods

This study was performed using the baseline data of 8138 participants in the pars cohort study who had no history of CVD and stroke. The updated 2019 WHO model was used to determine the 10-year fatal and non-fatal CVD risks. In general, there are two types of new WHO risk prediction models for CVD. The scores were determined based on age, sex, smoking status, diabetes, Systolic Blood Pressure (SBP), and total cholesterol for the laboratory-based model and age, sex, smoking status, SBP, and Body Mass Index (BMI) for the non-laboratory-based model. The agreement of these two models was determined via kappa statistics for the classified risk (low: < 10%, moderate: 10–< 20%, high: ≥ 20%). Correlation coefficients (r) and scatter plots was used for correlation between scores.

Results

The results revealed very strong correlation coefficients for all sex and age groups (r = 0.84 for males < 60 years old, 0.93 for males ≥ 60 years old, 0.85 for females < 60 years old, and 0.88 for females ≥ 60 years old). In the laboratory-based model, low, moderate, and high risks were 76.10%, 18.17%, and 5.73%, respectively. These measures were respectively obtained as 77.00%, 18.08%, and 4.92% in the non-laboratory-based model. Based on risk classification, the agreement was substantial for males < 60 years old and for both males and females aged ≥ 60 years (kappa values: 0.79 for males < 60 years old, 0.65 for males ≥ 60 years old, and 0.66 for females ≥ 60 years old) and moderate for females < 60 years old (kappa = 0.46).

Conclusions

The non-laboratory-based risk prediction model, which is simple, inexpensive, and non-invasive, classifies individuals almost identically to the laboratory-based model. Therefore, in countries with limited resources, these two models can be used interchangeably.

Abdominal obesity and myocardial infarction risk - We demonstrate the anthropometric and mathematical reasons that justify the association bias of the waist-to-hip ratio

Introduction

Antecedentes: el índice cintura-cadera (ICC) se utiliza ampliamente para evaluar la asociación de la obesidad abdominal con el infarto de miocardio (IM). Objetivo: nuestro propósito era determinar si el riesgo asociado a la ICC produce sesgo. Métodos: estudio de casos y controles en 252 varones. Usamos la estratificación como criterio para eliminar los efectos del sesgo. Creamos una covariable basal (ICC0,95-0,99) para una nueva muestra emparejada en el estrato de valores entre 0,95 y 0,99. Este estrato coincide con el área común de solapamiento de la distribución de puntos, donde todos los sujetos tienen un índice de propensión similar. Consideramos otra covariable (ICCS) condicionada en ICC < 1 y una circunferencia de cintura (CC) donde la asignación de riesgo fuera espúrea. Hipotetizamos que restando CC del valor de la cadera se calculaba otra variable aritmética (DCC) que podría ser esencial para evidenciar el efecto de confusion que genera el ICC. Resultados: IMC: ABC: 0,694, IC 95 % (0,628-0,760); OR: 3,8. CC: ABC: 0,743, IC 95 % (0,681-0,805); OR: 5,7. ICC: ABC: 0,798, IC 95 % (0,740-0,855); OR: 8,6. Índice cintura-talla (ICT): ABC: 0,782, IC 95 % (0,724-0,840); OR: 8,5. DCC: ABC: 0,204, IC 95 % (0,146-0,261); OR: 0,36. Prevalencia en los casos: ICC ≥ 0,95 (84,1 % vs. 38 %; OR: 8,6); ICC < 1 (36,3 % vs. 85,7 %; OR: 2,3); ICC ≥ 1 (63,4 % vs. 14,2 %; OR: 4,4); CC ≥ 94,4 (71,4 % vs. 30,1 %; OR: 5,7); DCC ≥ 2,2 (27,7 % vs. 75,3 %; OR: 7,9); ICCs (50 % vs. 25 %; OR: 2). Conclusiones: el ICC produce un sesgo de asociación en los casos de IM. Ello puede extrapolarse a otras poblaciones de estudio. El sesgo se explica por un error de concepto matemático que sobreestima el efecto protector de la cadera con respecto a la CC y la altura. El riesgo asociado al ICC por encima del de la CC o el ICT presenta inconsistencia antropométrica y sesgo, llegando a ser epidemiológicamente falso.