Obesity: BAI as a new measure of adiposity--throw away your scale?

Causal associations of anthropometric measurements with osteoarthritis: A Mendelian randomization study

OBJECTIVE

We believe that there is a causal relationship between waist circumference and knee osteoarthritis. To confirm the hypothesis, we have conducted this study.

METHODS

Genetic variants associated with the five anthropometric variables were obtained from previous large-scale genomewide association studies. Summary-level data on osteoarthritis were obtained from the UK Biobank. The univariable and multivariable MR framework were used to evaluate the associations. The two-sided p value was considered to be statistically significant at 0.01 (where p = 0.05/5) after Bonferroni correction for the five exposure variables.

RESULTS

In the univariable MR, there was evidence of a detrimental effect of height, weight, BMI, waist circumference, and hip circumference on osteoarthritis risk in the main IVW analyses (height: OR 1.115, 95% CI 1.054-1.180; weight: OR 1.765, 95% CI 1.650-1.889; BMI: OR 1.952, 95%CI 1.841-2.068; waist circumference: OR 2.140, 95% CI 1.994-2.296; hip circumference: OR 1.719, 95% CI 1.600-1.846). And the analyses on knee osteoarthritis and hip osteoarthritis yielded similar results. However, the multivariable MR showed that only waist circumference was causally associated with osteoarthritis, after adjusting for the confounding exposure effects (waist circumference: OR 1.877, 95% CI 1.286-2.739). Such association was also repeated in the analyses on knee osteoarthritis but not hip osteoarthritis.

CONCLUSION

Our study highlighted the causal associations between waist circumference and knee osteoarthritis risk.

Associations of Computed Tomography Image-Assessed Adiposity and Skeletal Muscles with Triple-Negative Breast Cancer

Obesity measured by anthropometrics is associated with increased risk of triple-negative breast cancer (TNBC). It is unclear to what extent specific adipose tissue components, aside from muscle, are associated with TNBC. This retrospective study included 350 breast cancer patients who received treatment between October 2011 and April 2020 with archived abdominal or pelvic computed tomography (CT) images. We measured the areas of adipose tissue and five-density levels of skeletal muscle on patients' third lumbar vertebra (L3) image. Logistic regression was performed to examine the associations of specific adiposity and skeletal muscles components and a four-category body composition phenotype with the TNBC subtype. Results showed that higher vs. lower areas (3rd vs. 1st tertiles) of visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT) were associated with increased odds of TNBC vs. non-TNBC after adjusting for age, race, stage, tumor grade, tumor size, and skeletal muscle areas (adjusted odds ratio [AOR], 11.25 [95% CI = 3.46-36.52]) and (AOR, 10.34 [95% CI = 2.90-36.90]) respectively. Higher areas of low density muscle was also associated with increased odds of TNBC (AOR, 3.15 [95% CI = 1.05-10.98]). Compared to normal body composition (low adipose tissue/high muscle), high adiposity/high muscle was associated with higher odds of TNBC (AOR, 5.54 [95% CI = 2.12-14.7]). These associations were mainly in premenopausal women and among patients with the CT performed after breast cancer surgery. Specific adipose tissue and low-density muscle can be associated with the TNBC subtype in breast cancer patients. The direction of association warrants confirmation by prospective studies.

Brief communication: Body mass index, body adiposity index, and percent body fat in Asians

Human obesity is a growing epidemic throughout the world. Body mass index (BMI) is commonly used as a good indicator of obesity. Body adiposity index (BAI = hip circumference (cm)/stature (m)(1.5) - 18), as a new surrogate measure, has been proposed recently as an alternative to BMI. This study, for the first time, compares BMI and BAI for predicting percent body fat (PBF; estimated from skinfolds) in a sample of 302 Buryat adults (148 men and 154 women) living in China. The BMI and BAI were strongly correlated with PBF in both men and women. The correlation coefficient between BMI and PBF was higher than that between BAI and PBF for both sexes. For the linear regression analysis, BMI better predicted PBF in both men and women; the variation around the regression lines for each sex was greater for BAI comparisons. For the receiver operating characteristic (ROC) analysis, the area under the ROC curve for BMI was higher than that for BAI for each sex, which suggests that the discriminatory capacity of the BMI is higher than the one of BAI. Taken together, we conclude that BMI is a more reliable indicator of PBF derived from skinfold thickness in adult Buryats.

An f2 pig resource population as a model for genetic studies of obesity and obesity-related diseases in humans: design and genetic parameters

Obesity is a rising worldwide public health problem. Difficulties to precisely measure various obesity traits and the genetic heterogeneity in human have been major impediments to completely disentangle genetic factors causing obesity. The pig is a relevant model for studying human obesity and obesity-related (OOR) traits. Using founder breeds divergent with respect to obesity traits we have created an F2 pig resource population (454 pigs), which has been intensively phenotyped for 36 OOR traits. The main rationale for our study is to characterize the genetic architecture of OOR traits in the F2 pig design, by estimating heritabilities, genetic, and phenotypic correlations using mixed- and multi-trait BLUP animal models. Our analyses revealed high coefficients of variation (15–42%) and moderate to high heritabilities (0.22–0.81) in fatness traits, showing large phenotypic and genetic variation in the F2 population, respectively. This fulfills the purpose of creating a resource population divergent for OOR traits. Strong genetic correlations were found between weight and lean mass at dual-energy x-ray absorptiometry scanning (0.56–0.97). Weight and conformation also showed strong genetic correlations with slaughter traits (e.g., r_g between abdominal circumference and leaf fat at slaughtering: 0.66). Genetic correlations between fat-related traits and the glucose level vary between 0.35 and 0.74 and show a strong correlation between adipose tissue and impaired glucose metabolism. Our power calculations showed a minimum of 80% power for QTL detection for all phenotypes. We revealed genetic correlations at population level, for the first time, for several difficult to measure and novel OOR traits and diseases. The results underpin the potential of the established F2 pig resource population for further genomic, systems genetics, and functional investigations to unravel the genetic background of OOR traits.