A quick look at the noteworthy articles in bariatric and metabolic research
THIS MONTH’S TOPIC:
Measuring Obesity and Metabolic Health
Relationship among body fat percentage, body mass index, and all-cause mortality: a cohort study.
Padwal R, Leslie WD, Lix LM, Majumdar SR. Ann Intern Med. 2016 Mar 8. [Epub ahead of print]
Synopsis: Researchers from Manitoba, Canada, conducted an observational study to examine associations of body mass index (BMI) and body fat percentage (separately and together) with mortality.
Participants included adults aged 40 years or older referred for bone mineral density (BMD) testing. Participants had dual-energy x-ray absorptiometry (DXA), entered a clinical BMD registry, and were followed using linked administrative databases. Adjusted, sex-stratified Cox models were constructed. Body mass index and DXA-derived body fat percentage were divided into quintiles, with quintile 1 as the lowest, quintile 5 as the highest, and quintile 3 as the reference. The final cohort included 49,476 women (mean age, 63.5 years; mean BMI, 27.0 kg/m2; mean body fat, 32.1%) and 4,944 men (mean age, 65.5 years; mean BMI, 27.4 kg/m2; mean body fat, 29.5%). Death occurred in 4,965 women over a median of 6.7 years and 984 men over a median of 4.5 years. In fully adjusted mortality models containing both BMI and body fat percentage, low BMI (hazard ratio [HR], 1.44 [95% CI, 1.30 to 1.59] for quintile 1 and 1.12 [CI, 1.02 to 1.23] for quintile 2) and high body fat percentage (HR, 1.19 [CI, 1.08 to 1.32] for quintile 5) were associated with higher mortality in women. In men, low BMI (HR, 1.45 [CI, 1.17 to 1.79] for quintile 1) and high body fat percentage (HR, 1.59 [CI, 1.28 to 1.96] for quintile 5) were associated with increased mortality.
The researchers reported the following limitations of their study: All participants were referred for BMD testing, which may limit generalizability. Serial measures of BMD and weight were not used. Some measures, such as physical activity and smoking, were unavailable.
They concluded that low BMI and high body fat percentage are independently associated with increased mortality, findings that may help explain the counterintuitive relationship between BMI and mortality.
Body shape, adiposity index, and mortality in postmenopausal women: Findings from the Women’s Health Initiative.
Thomson CA, Garcia DO, Wertheim BC, et al. Obesity (Silver Spring). 2016 Mar 15. [Epub ahead of print]
Synopsis: The authors conducted a prospective cohort analysis in the Women’s Health Initiative to evaluate the relationship between adiposity indices, a body shape index (ABSI) and body adiposity index (BAI), and mortality in 77,505 postmenopausal women. ABSI (waist circumference (cm)/[BMI2/3 × height (cm)1/2 ]), BAI (hip circumference (cm)/[height (m)1.5 ] – 18), weight, BMI, and waist circumference (WC) were evaluated in relation to mortality risk using adjusted Cox proportional hazards regression models. ABSI showed a linear association with mortality (HR, 1.37; 95% CI, 1.28-1.47 for quintile 5 vs. 1) while BMI and BAI had U-shaped relationships with HR of 1.30; 95% CI, 1.20-1.40 for obesity II/III BMI and 1.06, 95% CI, 0.99-1.13 for BAI. Higher WC (HR, 1.21; 95% CI, 1.13-1.29 for quintile 5 vs. 1) showed relationships similar to BMI.
The authors concluded that ABSI appears to be a clinically useful measure for estimating mortality risk, perhaps more so than BAI and BMI in postmenopausal women.
Indirect measure of visceral adiposity ‘A Body Shape Index’ (ABSI) is associated with arterial stiffness in patients with type 2 diabetes.
Bouchi R, Asakawa M, Ohara N, et al. BMJ Open Diabetes Res Care. 2016;4(1):e000188.
Synopsis: Among indirect measures of visceral adiposity, A Body Shape Index (ABSI), which is defined as waist circumference (WC)/(body mass index (BMI)(2/3)×height(1/2)), is unique in that ABSI is positively correlated with visceral adiposity and is supposed to be independent of BMI. ABSI has been also shown to be linearly and positively associated with visceral fat mass and all-cause and cardiovascular disease (CVD) in the general population. It is, however, uncertain whether ABSI could be associated with arterial stiffness in patients with diabetes.
This is a cross-sectional study of 607 patients with type 2 diabetes (mean age 64±12 years; 40.0% female). Visceral fat area (VFA, cm(2)) and subcutaneous fat area (SFA, cm(2)) were assessed with a dual-impedance analyzer. In order to estimate the risk for CVD, brachial-ankle pulse wave velocity (baPWV, cm) was used for the assessment of arterial stiffness.
ABSI was significantly and positively correlated with VFA (r=0.138, p=0.001) and negatively associated with BMI (r=-0.085, p=0.037). The correlation of z-score for ABSI with VFA remained significant (r=0.170, p<0.001) but not with BMI (r=0.009, p=0.820). ABSI (standardized β 0.095, p=0.043) but not WC (standardized β -0.060, p=0.200) was significantly and positively correlated with baPWV in the multivariate model including BMI as a covariate.
The authors concluded that ABSI appears to reflect visceral adiposity independently of BMI and to be a substantial marker of arterial stiffening in patients with type 2 diabetes.
The association of incident hypertension with metabolic health and obesity status: definition of metabolic health does not matter.
Kang YM, Jung CH, Jang JE, et al. Clin Endocrinol (Oxf). 2016 Apr 1. [Epub ahead of print]
Synopsis: The researchers aimed to investigate whether the metabolically healthy obese (MHO) phenotype is associated with future development of incident hypertension in a Korean population according to various definitions of metabolic health. The study population comprised 31,033 Koreans without hypertension. Participants were stratified into metabolically healthy nonobese (MNHO), metabolically unhealthy nonobese (MUNO), MHO, and metabolically unhealthy obese (MUO) by body mass index (cut-off value, 25.0kg/m2 ) and metabolic health state, using four different definitions: Adult Treatment Panel (ATP)-III, Wildman, Karelis, and the homeostasis model assessment (HOMA) criteria.
Over the median follow-up period of 35.0 months (range, 4.5-81.4 months), 4,589 of the 31,033 individuals (14.8%) developed incident hypertension. Compared with the MHNO group, the MHO group showed increased association with incident hypertension with multivariate-adjusted odds ratios of 1.56 (95% confidence interval [CI], 1.41-1.72), 1.58 (95% CI 1.42-1.75), 1.52 (95% CI 1.35-1.71), and 1.46 (95% CI 1.33-1.61), when defined by ATP-III, Wildman, Karelis, and HOMA criteria, respectively.
The authors concluded that MUO individuals showed the highest association with the incident hypertension (adjusted odds ratios up to 2.00). MHO subjects showed an approximately 1.5-fold higher association with incident hypertension than their nonobese counterpart regardless of the definition of metabolic health used. Thus, considering both metabolic health and obesity is important for the assessment of potential cardiovascular outcomes.
Universal equation for estimating ideal body weight and body weight at any BMI.
Peterson CM, Thomas DM, Blackburn GL, Heymsfield SB. Am J Clin Nutr. 2016 Mar 30. pii: ajcn121178. [Epub ahead of print]
Synopsis: The researchers merged the concepts of a linear Ideal body weight (IBW) equation and of defining target body weights in terms of body mass index (BMI).
With the use of calculus and approximations, they derived an easy-to-use linear equation that clinicians can use to calculate both IBW and body weight at any target BMI value. They measured the empirical accuracy of the equation with the use of NHANES data and performed a comparative analysis with past IBW equations. The linear equation allowed them to calculate body weights for any BMI and height with a mean empirical accuracy of 0.5–0.7% on the basis of The National Health and Nutrition Examination Survey (NHANES) data. The researchers also showed that their body weight equation directly aligns with BMI values for both men and women, which avoids the overestimation and underestimation problems at the upper and lower ends of the height spectrum that have plagued past IBW equations.
They concluded that their linear equation increases the sophistication of IBW equations by replacing them with a single universal equation that calculates both IBW and body weight at any target BMI and height. This equation is compatible with BMI and can be applied with the use of mental math or a calculator without the need for an app, which makes it a useful tool for both health practitioners and the general public.
Dynamic association of mortality hazard with body shape.
Krakauer NY, Krakauer JC. PLoS One. 2014;20;9(2):e88793.
Synopsis: In this article, the authors evaluate A Body Shape Index (ABSI) z score relative to population normals as a predictor of all-cause mortality over 24 years of follow-up to the British Health and Lifestyle Survey (HALS), They found that ABSI is a strong indicator of mortality hazard in this population, with death rates increasing by a factor of 1.13 (95% confidence interval, 1.09-1.16) per standard deviation increase in ABSI and a hazard ratio of 1.61 (1.40-1.86) for those with ABSI in the top 20% of the population compared to those with ABSI in the bottom 20%. Using the United States National Health and Nutrition Examination Survey (NHANES) normals to compute ABSI z scores gave similar results to using z scores derived specifically from the HALS sample. ABSI outperformed as a predictor of mortality hazard other measures of abdominal obesity such as waist circumference, waist to height ratio, and waist to hip ratio. Moreover, it was a consistent predictor of mortality hazard over at least 20 years of follow-up. Change in ABSI between two HALS examinations 7 years apart also predicted mortality hazard: individuals with a given initial ABSI who had rising ABSI were at greater risk than those with falling ABSI.
The authors concluded that ABSI is a readily computed dynamic indicator of health whose correlation with lifestyle and with other risk factors and health outcomes warrants further investigation.
‘Metabolically healthy obesity’: origins and implications.
Denis GV, Obin MS. Mol Aspects Med. 2013;34(1):59–70.
Synopsis: Two important clinical populations have been valuable to understand the mechanisms behind this conundrum: individuals who exhibit metabolic dysfunction, diabetes and elevated cardiovascular disease risk despite a lean body type, and individuals who are relatively protected from these dangers despite significant obesity. Study of this second group of ‘metabolically healthy obese’ people in particular has been revealing because such individuals exhibit specific, identifiable, anatomic, cellular and molecular features that set them apart from the rest of us who suffer declining health with increasing weight. Here, the authors examine some of these features, including some mouse models that are informative of mechanism, and suggest hypotheses for further study, including the possibility that genes and pathways of the immune system might offer new diagnostic or therapeutic targets.