Comparison of regional fat measurements by dual-energy X-ray absorptiometry and conventional anthropometry and their association with markers of diabetes and cardiovascular disease risk

Vasan, Senthil K.; Osmond, Clive; Canoy, Dexter; Christodoulides, C; Neville, Matthew J.; Gravio, Chiara Di; Fall, Caroline H.D.; Karpe, Fredrik

doi:10.1038/ijo.2017.289

Cited by 124 publications

(128 citation statements)

References 41 publications

Supporting

Mentioning

117

Contrasting

Unclassified

Order By: Relevance

“…This may be important for identifying the related cardiometabolic risk since, compared with subcutaneous, visceral adipose tissue promotes more vigorously obesity‐related inflammation through higher release of pro‐inflammatory adipokines and other factors (eg, monocyte chemoattractant protein‐1 and colony‐stimulating factor‐1) . Indeed, Vasan et al by measuring total and regional adiposity by dual energy X‐ray absorptiometry in 4950 apparently healthy participants, found that among upper adiposity depots, visceral adipose tissue showed stronger ORs for T2DM risk compared with WC, and concluded that typically used anthropometry may underestimate the associations of visceral adiposity with T2DM risk. Similarly, using ultrasonography in a population at high T2DM risk, visceral fat was found to significantly correlate with glucose intolerance and insulin resistance, even after adjustment for WC …”

Section: Discussionmentioning

confidence: 99%

Visceral adiposity index outperforms common anthropometric indices in predicting 10‐year diabetes risk: Results from the ATTICA study

Koloverou

Panagiotakos

Kyrou

et al. 2019

Diabetes Metabolism Res

View full text Add to dashboard Cite

Background Visceral adiposity index (VAI) is a novel marker of visceral adipose tissue accumulation and dysfunction. The study aim was to explore the association of VAI with the 10‐year type 2 diabetes mellitus (T2DM) incidence in apparently healthy individuals and compare its T2DM predictive ability against common anthropometric indices. Methods In 2001 to 2002, the ATTICA study (Greece) recruited a random sample of 1514 and 1528 CVD‐free men (18‐87 years old) and women (18‐89 years old), respectively. Sociodemographic, lifestyle, clinical, and biochemical characteristics of participants were measured at baseline, and the 10‐year follow‐up was performed during 2011 to 2012. After excluding participants with diabetes at baseline and participants without complete follow‐up information regarding diabetes status and/or baseline VAI values, the working sample consisted of 1049 participants. In this sample, the predictive value of baseline VAI value was studied in relation to 10‐year diabetes incidence. Results One hundred thirty‐three incident cases of diabetes were documented (10‐year incidence: 12.7%). In the fully adjusted model, VAI significantly increased diabetes risk by 22% (OR per 1‐unit increase =1.22; 95%CI, 1.09‐1.37). Markers of oxidative stress and inflammation were found to, at least partly, mediate this relationship. Also, a moderating effect of menstruation status was revealed among women. VAI showed the highest predictive ability and contributed the most, along with waist‐to‐height ratio, to the correct classification of participants who developed diabetes. Conclusions The present findings suggest that VAI may be a useful index for predicting long‐term diabetes development and may exhibit better predictive ability to commonly used anthropometric indices.

show abstract

Section: Discussionmentioning

confidence: 99%

Visceral adiposity index outperforms common anthropometric indices in predicting 10‐year diabetes risk: Results from the ATTICA study

Koloverou

Panagiotakos

Kyrou

et al. 2019

Diabetes Metabolism Res

View full text Add to dashboard Cite

show abstract

“…For all cohorts’ depot-specific fat mass was quantified using GE Lunar iDXA (GE Healthcare, Bucks, UK). As previously described[33] these give high precision estimates of body composition. The standard setting of the Encore software (version 14.0; GE Healthcare, Bucks, UK) was used to automatically define regions of interest ensuring that boundaries were consistent between cohorts.…”

Section: Methodsmentioning

confidence: 89%

“…All participants gave written, informed consent to participate, and studies were approved by the Oxfordshire Research Ethics Committee (08/H0606/107+5). A total of 3,281 individuals from the Oxford Biobank had both measures of fat mass with GE Lunar iDXA[33] and Illumina Human Exome Beadchip genotypes after QC checks. An additional 1,325 individuals contained DXA data and Affymetrix UK Biobank Axiom array genotype data from which overlapping ExomeChip data was extracted for the purposes of this study (Table 1).…”

Section: Methodsmentioning

confidence: 99%

“…A total of 1,145 individuals from the Fenland cohort had both measures of fatmass with GE Lunar iDXA[33] and Illumina Human Exome BeadChip genotypes after QC checks (Fenland-ExomeChip, Table 1), a further 997 had Illumina Infinium Core Exome data and 7,363 had Affymetrix UK Biobank Axiom array genotype data from which overlapping ExomeChip data was extracted for the purpose of this study (Table 1).…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Regional fat depot masses are influenced by protein-coding gene variants

Neville

Wittemans

Pinnick

et al. 2019

Preprint

View full text Add to dashboard Cite

23 With the identification of a large number of genetic loci associated with human fat 24 distribution and its importance for metabolic health, the question arises as to what 25 the genetic drivers for discrete fat depot expansion might be. To date most studies 26 have focussed on conventional anthropometric measures such as waist-to-hip ratio 27 (WHR) adjusted for body mass index. We searched for genetic loci determining 28 discrete fat depots mass size using an exome-wide approach in 3 large cohorts.29 Here we report an exome-wide analysis of non-synonymous genetic variants in 30 17,212 participants in which regional fat masses were quantified using dual-energy 31 X-ray absorptiometry. The missense variant CCDC92 S70C, previously associated with 32 WHR, is associated specifically with reduced visceral and increased leg fat masses.33 Allele-specific expression analysis shows that the deleterious minor allele carrying 34 transcript also has a constitutively higher expression. In addition, we identify two 35 variants associated with the transcriptionally distinct fat depot arm fat (SPATA20 K422R 36 and UQCC1 R51Q ). SPATA20 K422R , a rare novel locus with a large effect size specific 37 to arm, and UQCC1 R51Q , a common variant exome-wide significant in arm but 38 showing similar trends in other subcutaneous fat depots. In terms of the 39 understanding of human fat distribution, these findings suggest distinct regulation of 40 discrete fat depot expansion. 42Author summary 43 Human fat storing tissues are heterogeneous and comprise functionally and 44 structurally distinct regional fat depots, the relative size of which appear to have 45 significant implications for health. Whilst it is known that inter-individual differences in 46 fat distribution have genetic drivers, studies to date have focussed on crude 3 47 anthropometric approximations of region fat masses rather than precise measures.48 Here we describe an exome-wide analysis of a large collection of men and women 49 who have undergone body scanning using dual-energy X-ray absorptiometry (DXA) 50 to better define regional fat masses and identify new genetic drivers for human fat 51 distribution. With this approach we identify three gene regions associated with 52 distinct fat depots which can help to explain the variation in fat distribution between 53 people and may lead to a better understanding of the depot specific fat 54 tissue expansion.4 56 57 58 Beyond associations with chronic disease and overall obesity, as defined by body-59 mass index (BMI), it is becoming increasingly apparent that there is an even stronger 60 relationship between body fat distribution and cardio-metabolic disease [1, 2]. For 61 example, Yusuf et al. [1] showed that waist-to-hip ratio (WHR) is a stronger predict of 62 myocardial infarction than BMI. To date, the overwhelming majority genome and 63 exome-wide association studies on fat distribution have focussed on waist and hip 64 circumference and WHR [3, 4]. While these measures are easy and cheap to obtain 65 on a large sca...

show abstract

“…Computed tomography (CT), magnetic resonance imaging MRI), and dual-energy x-ray absorptiometry (DEXA) offer a significant advantage over conventional anthropometry in their ability to more accurately account for total adiposity, differentiate subcutaneous versus visceral adipose tissue, and detect foci of ectopic fat deposition in organs such as the liver and heart [52]. These imaging modalities have already demonstrated superiority to BMI in trials for the purposes of assessing risk [53] as well as evaluating response to weight loss therapy [54]. However, the cost of these diagnostic tools remains a significant drawback, preventing more widespread adoption of imaging into current clinical practice.…”

Section: Assessment Of Adipositymentioning

confidence: 99%

Trends in Obesity and Risk of Cardiovascular Disease

Lahey

Khan

2018

Curr Epidemiol Rep

View full text Add to dashboard Cite

Purpose of review: The obesity epidemic is a global health crisis of staggering proportion. Excess body weight is a major risk factor for the development of cardiovascular disease (CVD). We review temporal trends in obesity rates, pertinent pathophysiology to understand mechanisms of disease, and treatment strategies in the context of reducing cardiovascular risk. Recent findings: The prevalence of obesity is increasing in recent decades and is driven by a complex interplay of economic, environmental, and biological factors. In developed countries, changes in foodintake, such as increased consumption of energy-dense and added sugar have contributed significantly to weight gain. Single nucleotide variations in genes and alterations in the gut microbiome have been associated with the obese phenotype. The description of an obesity paradox in patients with CVD may have several explanations, including limitations of body mass index (BMI) to assess adiposity, selection bias, and lead-time bias with earlier onset of disease. Evidence-based treatments for weight loss include lifestyle intervention, pharmacotherapy, and bariatric surgery. Data on the long-term effects of these therapies on cardiovascular risk are limited. Summary: Overweight and obesity are associated with increased cardiovascular morbidity and mortality over the lifespan. Despite our increasing understanding of biological and environmental drivers of obesity, more work is needed in developing effective prevention strategies and implementation of evidence-based treatments to promote cardiovascular health and reduce cardiovascular risk. Ultimately, efforts to prevent and postpone cardiovascular morbidity should include focus on maintenance of normal BMI (primordial prevention) for a longer and healthier life, free of CVD.

show abstract

Comparison of regional fat measurements by dual-energy X-ray absorptiometry and conventional anthropometry and their association with markers of diabetes and cardiovascular disease risk

Cited by 124 publications

References 41 publications

Visceral adiposity index outperforms common anthropometric indices in predicting 10‐year diabetes risk: Results from the ATTICA study

Visceral adiposity index outperforms common anthropometric indices in predicting 10‐year diabetes risk: Results from the ATTICA study

Regional fat depot masses are influenced by protein-coding gene variants

Trends in Obesity and Risk of Cardiovascular Disease

Contact Info

Product

Resources

About