BMI, BMIfat, BAI or BAIFels – Which is the best adiposity index for the detection of excess weight?

Silva, Bruna Ramos; Mialich, Mirele Savegnago; Hoffman, Daniël J.; Jordão, Alceu Afonso

doi:10.20960/nh.366

Cited by 10 publications

(7 citation statements)

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“…Body mass index (BMI) was calculated as the weight in kilograms divided by the square of the height in meters. Body adiposity index (BAI), a surrogate measure of body fat, was calculated as described elsewhere (Silva et al 2017).…”

Section: Anthropometric Measurementsmentioning

confidence: 99%

ZBTB16 Gene Variability Influences Obesity-Related Parameters and Serum Lipid Levels in Czech Adults

Bendlová

Vaňková²,

Hill³

et al. 2017

Physiol Res

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The data derived from rat models and the preliminary results of human studies provide strong indices of involvement of common ZBTB16 variants in a range of cardiovascular and metabolic traits. This cross-sectional study in the Caucasian cohort of 1517 Czech adults aimed to verify the hypothesis that ZBTB16 gene variation directly affects obesity and serum lipid levels. Genotyping of nine polymorphisms of the ZBTB16 gene (rs11214863, rs593731, rs763857, rs2846027, rs681200, rs686989, rs661223, rs675044, rs567057) was performed. A multivariate bidirectional regression with the reduction of dimensionality (O2PLS model) revealed relationships between basal lipid levels and anthropometric parameters and some minor ZBTB16 alleles. In men, the predictors – age and presence of minor ZBTB16 alleles of rs686989, rs661223, rs675044, rs567057 – were associated with significantly higher body mass index, waist to hip ratio, body adiposity index, waist and abdominal circumferences, higher total cholesterol and LDL cholesterol and explained 20 % of variability of these variables. In women, the predictors – age and presence of the rs686989 minor T allele – were also associated with increased anthropometric parameters and total cholesterol and LDL cholesterol but the obtained O2PLS model explained only 7.8 % of the variability of the explained variables. Our study confirmed that the selected gene variants of the transcription factor ZBTB16 influence the obesity-related parameters and lipid levels. This effect was more pronounced in men.

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Section: Anthropometric Measurementsmentioning

confidence: 99%

ZBTB16 Gene Variability Influences Obesity-Related Parameters and Serum Lipid Levels in Czech Adults

Bendlová

Vaňková²,

Hill³

et al. 2017

Physiol Res

View full text Add to dashboard Cite

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“…This is consistent with studies in China (Wang et al, 2010), Turkey (Bozkirli et al, 2007) and India (Bhat et al, 2005) where some respondents with lower and normal BMIs were found to have higher percentages of adipose tissue. All these verified discrepancies in different populations urged Bergman et al (2011) and Silva et al (2016) to advocate for the use of the BIA method of assessing adiposity instead of the BMI. Based on gender, BMI and %BF showed higher prevalence of obesity among female participants than male participants, whereas for %VF obesity was higher among male participants than female.…”

Section: Discussionmentioning

confidence: 88%

A pilot study comparing bioelectrical impedance analysis and body mass index in determining obesity among staff of a Ghanaian University

Hayford

Appiah

Hassan

et al. 2019

Nutrition & Food Science

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Purpose -In Ghana, the body mass index (BMI) is widely used in clinical practice in assessing weight status, but it is limited as a measure of adiposity. The purpose of this study was to compare bioelectrical impedance analysis (BIA) and body mass index (BMI) methods in determining obesity among some Ghanaians.Design/methodology/approach -This was a cross-sectional survey involving 134 participants whose BMI were determined. Percentage body fat mass (%BF) and percentage visceral fat (%VF) were obtained by BIA using a hand-to-hand Omron body composition monitor with a weighing scale.Findings -Based on the WHO BMI criteria, 6.0 per cent of the participants were obese. However, according to BIA 18.7 and 20.9 per cent of the participants were obese according to % BF and %VF, respectively. The BMI and %BF showed higher prevalence of obesity among female participants (8.2 and 34.4 per cent, respectively) than male participants (4.1 and 5.5 per cent, respectively), whereas for %VF, obesity was higher among male participants than female participants (26.0 per cent, 14.8 per cent). There was significant positive correlation between BMI and % BF (r = 0.604, p = 0.001); and between BMI and %VF (r = 0.555, p = 0.001). NFS 49,2 240Research limitations/implications -There are discrepancies in the prevalence of obesity in the study population as measured by BMI and BIA methods. This suggests that the BMI and BIA may not be reliable tools for assessing obesity in this population. Further studies are needed to determine the cut-offs for BMI and BIA that are associated with metabolic risk in the population. The small sample size limits the generalizability of findings of this study.Originality/value -Body composition tends to vary by ethnicity and race; hence, it is essential to determine the appropriate tool for assessing adiposity in African populations for prompt and targeted interventions.

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“…Furthermore, we did not have sufficient power to perform a traditional mediation analysis. Self-reported exposure data may introduce misclassification bias, and there are known limitations in using BMI to (53). The study population comprised predominately older, non-Hispanic white participants, thus our results may not be generalizable to other age or racial/ethnic groups.…”

Section: Discussionmentioning

confidence: 91%

Prospective Association of Energy Balance Scores Based on Metabolic Biomarkers with Colorectal Cancer Risk

Guinter

Gapstur

McCullough

et al. 2020

Cancer Epidemiology, Biomarkers &Amp; Prevention

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Background: Energy balance-related factors, such as body mass index (BMI), diet, and physical activity, may influence colorectal cancer etiology through interconnected metabolic pathways, but their combined influence is less clear.Methods: We used reduced rank regression to derive three energy balance scores that associate lifestyle factors with combinations of prediagnostic, circulating levels of high-sensitivity C-reactive protein (hsCRP), C-peptide, and hemoglobin A 1c (HbA 1c ) among 2,498 participants in the Cancer Prevention Study-II Nutrition Cohort. Among 114,989 participants, we verified 2,228 colorectal cancer cases. We assessed associations of each score with colorectal cancer incidence and by tumor molecular phenotypes using Cox proportional hazards regression.Results: The derived scores comprised BMI, physical activity, screen time, and 14 food groups, and explained 5.1% to 10.5% of the variation in biomarkers. The HR and 95% confidence interval (CI) for quartile 4 versus 1 of the HbA 1c þC peptide-based score and colorectal cancer was 1.30 (1.15-1.47), the hsCRP-based score was 1. 35 (1.19-1.53), and the hsCRP, C-peptide, and HbA 1c -based score was 1.35 (1.19-1.52). The latter score was associated with non-CIMP tumors (HR Q4vsQ1 : 1.59; 95% CI: 1.17-2.16), but not CIMP-positive tumors (P heterogeneity ¼ 0.04).Conclusions: These results further support hypotheses that systemic biomarkers of metabolic health-inflammation and abnormal glucose homeostasis-mediate part of the relationship between several energy balance-related modifiable factors and colorectal cancer risk.Impact: Results support cancer prevention guidelines for maintaining a healthful body weight, consuming a healthful diet, and being physically active. More research is needed on these clusters of exposures with molecular phenotypes of tumors.

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BMI, BMIfat, BAI or BAIFels – Which is the best adiposity index for the detection of excess weight?

Cited by 10 publications

References 0 publications

ZBTB16 Gene Variability Influences Obesity-Related Parameters and Serum Lipid Levels in Czech Adults

ZBTB16 Gene Variability Influences Obesity-Related Parameters and Serum Lipid Levels in Czech Adults

A pilot study comparing bioelectrical impedance analysis and body mass index in determining obesity among staff of a Ghanaian University

Prospective Association of Energy Balance Scores Based on Metabolic Biomarkers with Colorectal Cancer Risk

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