Search or use up and Body fat boundary bondary keys to select an item. Marcus D. Estimation of total body fat and protein by densitometry. and O.

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A person's waist-to-height ratio WHtR , occasionally written WtHR or called waist-to-stature ratio WSR , is defined as their waist circumference divided by their height, both measured in the same units.

It is used as a predictor of obesity-related cardiovascular disease. The WHtR is a measure of the distribution of body fat. Higher values of WHtR indicate higher risk of obesity-related cardiovascular diseases; it is correlated with abdominal obesity.

More than twenty-five years ago, waist-to-height ratio WHtR was first suggested as a simple health risk assessment tool because it is a proxy for harmful central adiposity [2] and a boundary value of 0. According to World Health Organization guidance, the waist circumference is usually measured midway between the lower rib and the iliac crest.

In April , the UK's National Institute for Health and Care Excellence a government body proposed new guidelines which suggested that all adults "ensure their waist size is less than half their height in order to help stave off serious health problems".

The October NICE guidelines have suggested boundary values for WHtR defining the degree of central adiposity as follows:. NICE say that these classifications can be used for people with a body mass index BMI of under 35, for both sexes and all ethnicities , including adults with high muscle mass.

The health risks associated with higher levels of central adiposity include type 2 diabetes , hypertension and cardiovascular disease. NICE have proposed the same boundary values for children of 5 years and over.

Boundary values were first suggested for WHtR in to reflect health implications and were portrayed on a simple chart of waist circumference against height. The 0. The first boundary value for increased risk of WHtR 0.

WHtR is a proxy for central visceral or abdominal adiposity : values of WHtR are significantly correlated with direct measures of central visceral or abdominal adiposity using techniques such as CT , MRI or DEXA.

WHtR is an indicator of 'early health risk': several systematic reviews and meta-analyses of data in adults of all ages, [18] [19] [20] [21] as well as in children and adolescents, [22] [23] have supported the superiority of WHtR over the use of BMI and waist circumference in predicting early health risk.

Cross-sectional studies in many different global populations have supported the premise that WHtR is a simple and effective anthropometric index to identify health risks in adults of all ages [19] [20] [24] [25] [26] [27] [28] [29] [30] [31] [32] [33] [34] [35] and in children and adolescents.

In a comprehensive narrative review, Yoo concluded that "additional use of WHtR with BMI or WC may be helpful because WHtR considers both height and central obesity.

WHtR may be preferred because of its simplicity and because it does not require sex- and age-dependent cut-offs". Not only does WHtR have a close relationship with morbidity , it also has a clearer relationship with mortality than BMI. Many cross- sectional studies have shown that, even within the normal BMI range, many adults have WHtR which is above 0.

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PLoS ONE 11 , e Karlsson, T. Contribution of genetics to visceral adiposity and its relation to cardiovascular and metabolic disease. Kaess, B. The ratio of visceral to subcutaneous fat, a metric of body fat distribution, is a unique correlate of cardiometabolic risk.

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Gonzaga-Jauregui, C. Clinical and molecular prevalence of lipodystrophy in an unascertained large clinical care cohort.

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Endocrine 64 , — Sekizkardes, H. Efficacy of metreleptin treatment in familial partial lipodystrophy due to PPARG vs LMNA pathogenic variants. Stanley, T.

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Download references. This work was supported by the Sarnoff Cardiovascular Research Foundation Fellowship to S. from the National Human Genome Research Institute, a Hassenfeld Scholar Award from Massachusetts General Hospital to A.

are listed as co-inventors on a patent application for the use of imaging data in assessing body fat distribution and associated cardiometabolic risk. Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA.

Marcus D. Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA, USA. Saaket Agrawal, Patrick T.

Center for Genomic Medicine, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA. Department of Medicine, Harvard Medical School, Boston, MA, USA. Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard, Cambridge, MA, USA. Center for Computational Health, IBM Research, Cambridge, MA, USA.

You can also search for this author in PubMed Google Scholar. and S. contributed equally. and A. jointly supervised this work. Conceptualization: M.

Methodology: M. Investigation: M. Funding acquisition: P. Supervision: P. Writing: M. Correspondence to Amit V. The authors declare no competing non-financial interests, but the following competing financial interests: M.

are supported by grants from Bayer AG applying machine learning in cardiovascular disease. has served as a scientific consultant to Third Rock Ventures.

receives sponsored research support from Bayer AG and has consulted for Bayer AG, Novartis, MyoKardia and Quest Diagnostics. is also employed as a Venture Partner at GV and consulted for Novartis; and has received funding from Intel, Verily and MSFT.

is an employee of IBM Research. B serves as a consultant for Novartis. is an employee and holds equity in Verve Therapeutics; has served as a scientific advisor to Amgen, Maze Therapeutics, Navitor Pharmaceuticals, Sarepta Therapeutics, Novartis, Silence Therapeutics, Korro Bio, Veritas International, Color Health, Third Rock Ventures, Illumina, Foresite Labs, and Columbia University NIH ; received speaking fees from Illumina, MedGenome, Amgen, and the Novartis Institute for Biomedical Research; and received a sponsored research agreement from IBM Research.

Open Access This article is licensed under a Creative Commons Attribution 4. Reprints and permissions. Klarqvist, M. Silhouette images enable estimation of body fat distribution and associated cardiometabolic risk. Download citation. Received : 18 January Accepted : 06 July Published : 27 July Anyone you share the following link with will be able to read this content:.

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Skip to main content Thank you for visiting nature. nature npj digital medicine articles article. Download PDF. Subjects Computer science Medical imaging Obesity. Abstract Inter-individual variation in fat distribution is increasingly recognized as clinically important but is not routinely assessed in clinical practice, in part because medical imaging has not been practical to deploy at scale for this task.

Introduction Body-mass index BMI is a routinely measured proxy for overall fat burden. Results Silhouettes allow for accurate estimation of VAT, ASAT, and GFAT volumes In all, 40, participants of the UK Biobank imaging substudy with VAT, ASAT, and GFAT volumes previously quantified on the basis of MRI were included 14 , 29 , 30 , 31 , Table 1 Characteristics of the study population.

Full size table. Full size image. Methods Study population All analyses were conducted in the UK Biobank, a prospective cohort study that recruited over , individuals aged 40—69 in the UK from to Preparing silhouettes from whole-body magnetic resonance images Whole-body MRI data was preprocessed as previously described Deep learning to predict fat depot volumes using silhouettes For predicting the target fat depot volumes, we employed the DenseNet architecture as the base model Linear anthropometric models to benchmark performance Sex-specific anthropometric models were generated by predicting each MRI-derived fat measurement using one of, or a combination of, age, weight, height, body-mass index BMI , waist circumference, hip circumference, waist-to-hip ratio WHR , and five bioelectric impedance measurements commonly used for measuring body fat.

Association with cardiometabolic diseases The primary outcomes were prevalent and incident type 2 diabetes and coronary artery disease, and prevalent hypertension and hypercholesterolemia Reporting summary Further information on research design is available in the Nature Research Reporting Summary linked to this article.

Data availability The raw UK Biobank data—including the anthropometric data reported here—are made available to researchers from universities and other research institutions with genuine research inquiries following IRB and UK Biobank approval.

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Author information Author notes These authors contributed equally: Marcus D. Klarqvist, Saaket Agrawal. These authors jointly supervised this work: Puneet Batra, Amit V.

Authors and Affiliations Data Sciences Platform, Broad Institute of MIT and Harvard, Cambridge, MA, USA Marcus D. Khera Department of Medicine, Harvard Medical School, Boston, MA, USA Saaket Agrawal, Patrick T.

Khera Eric and Wendy Schmidt Center, Broad Institute of MIT and Harvard, Cambridge, MA, USA Anthony Philippakis Center for Computational Health, IBM Research, Cambridge, MA, USA Kenney Ng Verve Therapeutics, Cambridge, MA, USA Amit V. Khera Authors Marcus D. Klarqvist View author publications.

View author publications. Ethics declarations Competing interests The authors declare no competing non-financial interests, but the following competing financial interests: M. Supplementary information.

Supplementary Information. Supplementary Data Reporting Summary. Rights and permissions Open Access This article is licensed under a Creative Commons Attribution 4. About this article. Cite this article Klarqvist, M. Copy to clipboard. About the journal Aims and scope Content types Journal Information About the Editors Contact Editorial policies Calls for Papers Journal Metrics About the Partner Open Access Early Career Researcher Editorial Fellowship Editorial Team Vacancies.

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Thank you for visiting nature. You are using a browser version Body fat boundary limited support Body fat boundary CSS. Body fat boundary obtain the best Bodu, we recommend buondary use fst more up to date browser or turn Fast metabolism boosters compatibility houndary in Internet Explorer. In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Inter-individual variation in fat distribution is increasingly recognized as clinically important but is not routinely assessed in clinical practice, in part because medical imaging has not been practical to deploy at scale for this task. Two-dimensional coronal and sagittal silhouettes are constructed from whole-body magnetic resonance images in 40, participants of the UK Biobank and used as inputs for a convolutional neural network to predict each of these quantities.

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