How to best assess abdominal obesity

Fang, Hongjuan; Berg, Elizabeth; Cheng, Xiaoguang; Shen, Wei

doi:10.1097/mco.0000000000000485

Cited by 176 publications

(178 citation statements)

References 45 publications

(54 reference statements)

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“…35,36 Investigators were equally keen on determining the optimal cut-off points for BF%. 37,38 Some confusion was caused by the authors referring to WHO report pertaining to the cut-offs for BF%, indicative of prevalence of obesity above 25% for men, and 35% for women. 39 In point of fact, the cut-offs values published by WHO were not meant as the strict guidelines to be followed in diagnosing obesity, as is the case with BMI, nor were they intended to be construed as the CRFs risk predictors.…”

Section: Discussionmentioning

confidence: 99%

<p>Optimal Body Fat Percentage Cut-Off Values in Predicting the Obesity-Related Cardiovascular Risk Factors: A Cross-Sectional Cohort Study</p>

Macek¹,

Biskup²,

Terek-Derszniak³

et al. 2020

DMSO

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Background: Reliable obesity assessment is essential in evaluating the risk of cardiovascular risk factors (CRFs). Non-availability of clearly defined cutoffs for body fat percentage (BF%), as well as a widespread application of surrogate measures for obesity assessment, may result in incorrect prediction of cardio-metabolic risk. Purpose: The study aimed to determine optimal cutoff points for BF%, with a view of predicting the CRFs related to obesity. Patients and Methods: The study involved 4735 (33.6% of men) individuals, the Polish-Norwegian Study (PONS) participants, aged 45-64. BF% was measured with the aid of bioelectrical impedance analysis (BIA) method. The gender-specific cutoffs of BF% were found with respect to at least one CRF. A P-value approach, and receiver operating characteristic curve analyses were pursued for BF% cutoffs , which optimally differentiated normal from the risk groups. The associations between BF% and CRFs were determined by logistic regression models. Results: The cutoffs for BF% were established as 25.8% for men and 37.1% for women. With the exception of dyslipidemia, in men and women whose BF% was above the cutoffs , the odds for developing CRFs ranged 2-4 times higher than those whose BF% was below the cutoffs. Conclusion: Controlling BF% below the thresholds indicating an increased health hazard may be instrumental in appreciably reducing overall exposure to developing cardiometabolic risk.

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Section: Discussionmentioning

confidence: 99%

<p>Optimal Body Fat Percentage Cut-Off Values in Predicting the Obesity-Related Cardiovascular Risk Factors: A Cross-Sectional Cohort Study</p>

Macek¹,

Biskup²,

Terek-Derszniak³

et al. 2020

DMSO

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“…Furthermore, abdominal obesity is difficult to diagnose in routine clinical care because it requires access to computed tomography 5 or magnetic resonance imaging 6 for precise quantification. Anthropometric measures of abdominal obesity include waist circumference, waist-to-height ratio, waist-to-hip ratio, and the conicity index 7,8 . Thus, the most commonly used surrogate to diagnose abdominal obesity in clinical care and research examinations is waist circumference 9,10 .…”

Section: Introductionmentioning

confidence: 99%

Altitude and its inverse association with abdominal obesity in an Andean country: a cross-sectional study

et al. 2019

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Background: Abdominal obesity represents an accurate predictor of overall morbidity and mortality, which is worrisome because it is also continuously increasing across Andean countries. However, its relationship with altitude remains unclear. The objective of this study was to assess the association between altitude and abdominal obesity in Peru, and how sociodemographic variables impact this association. Methods: We estimated the prevalence of abdominal obesity in Peru and analyzed its association with altitude using the data from the 2012-2013 National Household Survey (ENAHO). During this survey, a representative sample of Peruvians was screened for abdominal obesity, using waist circumference as a proxy, and the Adult Treatment Panel III guidelines cutoffs. Results: Data were analyzed from a sample of 20 489 Peruvians (51% male). The prevalence of abdominal obesity was estimated at 33.6% (95% CI: 32.5 to 34.6%). In Peru, altitude was significantly and inversely associated with abdominal obesity, decreasing with higher altitudes: 1500-2999 meters above mean sea level (MAMSL) vs <1500 MAMSL, adjusted prevalence rate [aPR]= 0.90 (95% CI: 0.84 to 0.96); ≥3000 MAMSL vs <1500 MAMSL, aPR= 0.78 (95% CI: 0.72 to 0.84), when adjusting by age, gender and residence area (rural/urban). However, this association was significantly modified by age and gender (p< 0.001). Conclusion: Abdominal obesity is highly prevalent in Peru and decreases significantly with altitude, but age and gender modify this association. Thus, abdominal obesity appears to affect older women from low altitudes more than younger men from high altitudes.

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“…At the other levels, adipose tissue is less‐well correlated with the entire volume of fatty tissue. This analysis technique is largely used in clinical research …”

Section: Discussionmentioning

confidence: 99%

“…SAT and VAT were determined at the L4 level with the OsiriX 5.7 software (Pixmeo ® ): The adipose tissue areas were first defined by all pixels with attenuation values comprised between −190 and −30 Hounsfield Units (HU); the peritoneum was then delineated; finally, the SAT was calculated as the extra‐peritoneal adipose area and the VAT as the intra‐peritoneal adipose area. The level chosen for the analysis was the L4 corresponding to the umbilical level, the aortic bifurcation, and the top of the iliac crests as usually referenced in the literature . The VSK was the distance from the anterior edge of the iliac vein at the level of its bifurcation to the linea semilunearis (musculo‐aponeurotic line of the transversus abdominis musculus ).…”

Section: Methodsmentioning

confidence: 99%

Temporary contraindication of obese recipients in kidney transplantation: A new morphometric tool for decision support

Pinar

Renard

Bedretdinova

et al. 2020

Clinical Transplantation

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Background Morbid obesity, based on body mass index (BMI) and/or clinical examination, can be a temporary contraindication (TCI) of kidney transplantation. However, BMI alone does not evaluate the intra‐ or extra‐peritoneal distribution of fatty tissue, and clinical examination alone is subjective. The objective was to evaluate the interest of morphometric criteria to ensure reproducible and consensual decision of TCI. Methods We retrospectively included patients with a BMI >30 transplanted or temporarily contraindicated because of their weight from 2012 to 2017. The following measurements were performed on CT scan sections using a semiautomatic Hounsfield density detection software: subcutaneous adipose tissue surface (SAT), visceral adipose tissue surface (VAT), vessel‐to‐skin distance (VSK), abdominal perimeter (AP), and psoas index. Performance of morphometric measures to predict TCI was assessed through ROC analysis. Results Ninety‐seven patients were included: 76 kidney transplant recipients and 21 on the TCI list. The area under the curve (AUC, 95%CI) for the BMI model to predict TCI was 0.81 (0.72‐0.90). A 5‐variable model including BMI, VAT, VSK, AP, and age gave an AUC of 0.88 (0.78‐0.98). Conclusions Morphometric obesity parameters are associated with TCI decision‐making for kidney transplantation: When combined with BMI in a “morphometric tool,” they were predictive of a TCI decision.

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How to best assess abdominal obesity

Cited by 176 publications

References 45 publications

<p>Optimal Body Fat Percentage Cut-Off Values in Predicting the Obesity-Related Cardiovascular Risk Factors: A Cross-Sectional Cohort Study</p>

<p>Optimal Body Fat Percentage Cut-Off Values in Predicting the Obesity-Related Cardiovascular Risk Factors: A Cross-Sectional Cohort Study</p>

Altitude and its inverse association with abdominal obesity in an Andean country: a cross-sectional study

Temporary contraindication of obese recipients in kidney transplantation: A new morphometric tool for decision support

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