{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T20:14:40Z","timestamp":1774901680493,"version":"3.50.1"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2022,7,27]],"date-time":"2022-07-27T00:00:00Z","timestamp":1658880000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2022,7,27]],"date-time":"2022-07-27T00:00:00Z","timestamp":1658880000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100013915","name":"Sarnoff Cardiovascular Research Foundation","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100013915","id-type":"DOI","asserted-by":"crossref"}]},{"name":"IBM Research"},{"DOI":"10.13039\/100000051","name":"U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute","doi-asserted-by":"publisher","award":["1K08HG010155"],"award-info":[{"award-number":["1K08HG010155"]}],"id":[{"id":"10.13039\/100000051","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000051","name":"U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute","doi-asserted-by":"publisher","award":["1U01HG011719"],"award-info":[{"award-number":["1U01HG011719"]}],"id":[{"id":"10.13039\/100000051","id-type":"DOI","asserted-by":"publisher"}]},{"name":"U.S. Department of Health & Human Services | NIH | National Human Genome Research Institute"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    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. Here, we report a deep learning model trained on an individual\u2019s body shape outline\u2014or \u201csilhouette\u201d \u2014that enables accurate estimation of specific fat depots of interest, including visceral (VAT), abdominal subcutaneous (ASAT), and gluteofemoral (GFAT) adipose tissue volumes, and VAT\/ASAT ratio. Two-dimensional coronal and sagittal silhouettes are constructed from whole-body magnetic resonance images in 40,032 participants of the UK Biobank and used as inputs for a convolutional neural network to predict each of these quantities. Mean age of the study participants is 65 years and 51% are female. A cross-validated deep learning model trained on silhouettes enables accurate estimation of VAT, ASAT, and GFAT volumes (\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    : 0.88, 0.93, and 0.93, respectively), outperforming a comparator model combining anthropometric and bioimpedance measures (\u0394\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    \u2009=\u20090.05\u20130.13). Next, we study VAT\/ASAT ratio, a nearly body-mass index (BMI)\u2014and waist circumference-independent marker of metabolically unhealthy fat distribution. While the comparator model poorly predicts VAT\/ASAT ratio (\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    : 0.17\u20130.26), a silhouette-based model enables significant improvement (\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    : 0.50\u20130.55). Increased silhouette-predicted VAT\/ASAT ratio is associated with increased risk of prevalent and incident type 2 diabetes and coronary artery disease independent of BMI and waist circumference. These results demonstrate that body silhouette images can estimate important measures of fat distribution, laying the scientific foundation for scalable population-based assessment.\n                  <\/jats:p>","DOI":"10.1038\/s41746-022-00654-1","type":"journal-article","created":{"date-parts":[[2022,7,29]],"date-time":"2022-07-29T13:45:46Z","timestamp":1659102346000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Silhouette images enable estimation of body fat distribution and associated cardiometabolic risk"],"prefix":"10.1038","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7630-2708","authenticated-orcid":false,"given":"Marcus D. R.","family":"Klarqvist","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2535-4759","authenticated-orcid":false,"given":"Saaket","family":"Agrawal","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1738-304X","authenticated-orcid":false,"given":"Nathaniel","family":"Diamant","sequence":"additional","affiliation":[]},{"given":"Patrick T.","family":"Ellinor","sequence":"additional","affiliation":[]},{"given":"Anthony","family":"Philippakis","sequence":"additional","affiliation":[]},{"given":"Kenney","family":"Ng","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6822-0593","authenticated-orcid":false,"given":"Puneet","family":"Batra","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6535-5839","authenticated-orcid":false,"given":"Amit V.","family":"Khera","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,7,27]]},"reference":[{"key":"654_CR1","doi-asserted-by":"publisher","first-page":"e277","DOI":"10.1016\/S2468-2667(17)30074-9","volume":"2","author":"M Kivim\u00e4ki","year":"2017","unstructured":"Kivim\u00e4ki, M. et al. Overweight, obesity, and risk of cardiometabolic multimorbidity: pooled analysis of individual-level data for 120\u2008813 adults from 16 cohort studies from the USA and Europe. Lancet Public Health 2, e277\u2013e285 (2017).","journal-title":"Lancet Public Health"},{"key":"654_CR2","doi-asserted-by":"publisher","first-page":"1625","DOI":"10.1056\/NEJMoa021423","volume":"348","author":"EE Calle","year":"2003","unstructured":"Calle, E. E. Overweight, obesity, and mortality from cancer in a prospectively studied cohort of U.S. adults. N. Engl. J. Med. 348, 1625\u20131638 (2003).","journal-title":"N. Engl. J. Med."},{"key":"654_CR3","doi-asserted-by":"publisher","first-page":"782","DOI":"10.7326\/M20-3214","volume":"173","author":"MR Anderson","year":"2020","unstructured":"Anderson, M. R. et al. Body mass index and risk for intubation or death in SARS-CoV-2 infection: a retrospective cohort study. Ann. Intern. Med. 173, 782\u2013790 (2020).","journal-title":"Ann. Intern. Med."},{"key":"654_CR4","first-page":"1","volume":"3","author":"P Gonz\u00e1lez-Muniesa","year":"2017","unstructured":"Gonz\u00e1lez-Muniesa, P. et al. Obesity. Nat. Rev. Dis. Prim. 3, 1\u201318 (2017).","journal-title":"Nat. Rev. Dis. Prim."},{"key":"654_CR5","doi-asserted-by":"publisher","first-page":"2569","DOI":"10.1210\/jc.2004-0165","volume":"89","author":"AD Karelis","year":"2004","unstructured":"Karelis, A. D., St-Pierre, D. H., Conus, F., Rabasa-Lhoret, R. & Poehlman, E. T. Metabolic and body composition factors in subgroups of obesity: what do we know? J. Clin. Endocrinol. Metab. 89, 2569\u20132575 (2004).","journal-title":"J. Clin. Endocrinol. Metab."},{"key":"654_CR6","doi-asserted-by":"publisher","first-page":"642","DOI":"10.1001\/archinte.167.7.642","volume":"167","author":"T McLaughlin","year":"2007","unstructured":"McLaughlin, T., Abbasi, F., Lamendola, C. & Reaven, G. Heterogeneity in the prevalence of risk factors for cardiovascular disease and type 2 diabetes mellitus in obese individuals: effect of differences in insulin sensitivity. Arch. Intern. Med. 167, 642\u2013648 (2007).","journal-title":"Arch. Intern. Med."},{"key":"654_CR7","doi-asserted-by":"publisher","first-page":"1617","DOI":"10.1001\/archinte.168.15.1617","volume":"168","author":"RP Wildman","year":"2008","unstructured":"Wildman, R. P. et al. The obese without cardiometabolic risk factor clustering and the normal weight with cardiometabolic risk factor clustering: prevalence and correlates of 2 phenotypes among the US population (NHANES 1999-2004). Arch. Intern. Med. 168, 1617\u20131624 (2008).","journal-title":"Arch. Intern. Med."},{"key":"654_CR8","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/j.metabol.2015.10.019","volume":"65","author":"H Mathew","year":"2016","unstructured":"Mathew, H., Farr, O. M. & Mantzoros, C. S. Metabolic health and weight: Understanding metabolically unhealthy normal weight or metabolically healthy obese patients. Metabolism 65, 73\u201380 (2016).","journal-title":"Metabolism"},{"key":"654_CR9","doi-asserted-by":"publisher","first-page":"292","DOI":"10.1016\/j.cmet.2017.07.008","volume":"26","author":"N Stefan","year":"2017","unstructured":"Stefan, N. & Schick, F. H\u00e4ring H-U. causes, characteristics, and consequences of metabolically unhealthy normal weight in humans. Cell Metab. 26, 292\u2013300 (2017).","journal-title":"Cell Metab."},{"key":"654_CR10","doi-asserted-by":"publisher","first-page":"616","DOI":"10.1016\/S2213-8587(20)30110-8","volume":"8","author":"N Stefan","year":"2020","unstructured":"Stefan, N. Causes, consequences, and treatment of metabolically unhealthy fat distribution. Lancet Diabetes Endocrinol. 8, 616\u2013627 (2020).","journal-title":"Lancet Diabetes Endocrinol."},{"key":"654_CR11","doi-asserted-by":"publisher","first-page":"1692","DOI":"10.1136\/bmj.290.6483.1692","volume":"290","author":"M Ashwell","year":"1985","unstructured":"Ashwell, M., Cole, T. J. & Dixon, A. K. Obesity: new insight into the anthropometric classification of fat distribution shown by computed tomography. Br. Med J. Clin. Res Ed. 290, 1692\u20131694 (1985).","journal-title":"Br. Med J. Clin. Res Ed."},{"key":"654_CR12","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1152\/physrev.00033.2011","volume":"93","author":"A Tchernof","year":"2013","unstructured":"Tchernof, A. & Despr\u00e9s, J.-P. Pathophysiology of human visceral obesity: an update. Physiol. Rev. 93, 359\u2013404 (2013).","journal-title":"Physiol. Rev."},{"key":"654_CR13","doi-asserted-by":"publisher","first-page":"715","DOI":"10.1016\/S2213-8587(19)30084-1","volume":"7","author":"IJ Neeland","year":"2019","unstructured":"Neeland, I. J. et al. Visceral and ectopic fat, atherosclerosis, and cardiometabolic disease: a position statement. Lancet Diabetes Endocrinol. 7, 715\u2013725 (2019).","journal-title":"Lancet Diabetes Endocrinol."},{"key":"654_CR14","doi-asserted-by":"publisher","unstructured":"Agrawal S, et al. Association of machine learning-derived measures of body fat distribution in >40,000 individuals with cardiometabolic diseases. Preprint at medRxiv https:\/\/doi.org\/10.1101\/2021.05.07.21256854 (2021).","DOI":"10.1101\/2021.05.07.21256854"},{"key":"654_CR15","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1038\/s41574-019-0310-7","volume":"16","author":"R Ross","year":"2020","unstructured":"Ross, R. et al. Waist circumference as a vital sign in clinical practice: a Consensus Statement from the IAS and ICCR Working Group on Visceral Obesity. Nat. Rev. Endocrinol. 16, 177\u2013189 (2020).","journal-title":"Nat. Rev. Endocrinol."},{"key":"654_CR16","doi-asserted-by":"publisher","first-page":"1298","DOI":"10.1038\/ejcn.2013.203","volume":"67","author":"X Song","year":"2013","unstructured":"Song, X. et al. Comparison of various surrogate obesity indicators as predictors of cardiovascular mortality in four European populations. Eur. J. Clin. Nutr. 67, 1298\u20131302 (2013).","journal-title":"Eur. J. Clin. Nutr."},{"key":"654_CR17","doi-asserted-by":"publisher","first-page":"2105","DOI":"10.1056\/NEJMoa0801891","volume":"359","author":"T Pischon","year":"2008","unstructured":"Pischon, T. et al. General and abdominal adiposity and risk of death in Europe. N. Engl. J. Med. 359, 2105\u20132120 (2008).","journal-title":"N. Engl. J. Med."},{"key":"654_CR18","doi-asserted-by":"publisher","first-page":"1293","DOI":"10.1001\/archinternmed.2010.201","volume":"170","author":"EJ Jacobs","year":"2010","unstructured":"Jacobs, E. J. et al. Waist circumference and all-cause mortality in a large US cohort. Arch. Intern. Med. 170, 1293\u20131301 (2010).","journal-title":"Arch. Intern. Med."},{"key":"654_CR19","doi-asserted-by":"publisher","first-page":"4668","DOI":"10.1118\/1.4926557","volume":"42","author":"B Xie","year":"2015","unstructured":"Xie, B. et al. Accurate body composition measures from whole-body silhouettes. Med. Phys. 42, 4668\u20134677 (2015).","journal-title":"Med. Phys."},{"key":"654_CR20","doi-asserted-by":"publisher","first-page":"6232","DOI":"10.1002\/mp.14492","volume":"47","author":"IY Tian","year":"2020","unstructured":"Tian, I. Y. et al. Predicting 3D body shape and body composition from conventional 2D photography. Med. Phys. 47, 6232\u20136245 (2020).","journal-title":"Med. Phys."},{"key":"654_CR21","doi-asserted-by":"publisher","first-page":"e0206430","DOI":"10.1371\/journal.pone.0206430","volume":"13","author":"O Affuso","year":"2018","unstructured":"Affuso, O. et al. A method for measuring human body composition using digital images. PLoS ONE 13, e0206430 (2018).","journal-title":"PLoS ONE"},{"key":"654_CR22","doi-asserted-by":"publisher","first-page":"920","DOI":"10.1038\/s41430-019-0501-2","volume":"74","author":"S Kennedy","year":"2020","unstructured":"Kennedy, S. et al. Optical imaging technology for body size and shape analysis: evaluation of a system designed for personal use. Eur. J. Clin. Nutr. 74, 920\u2013929 (2020).","journal-title":"Eur. J. Clin. Nutr."},{"key":"654_CR23","doi-asserted-by":"publisher","first-page":"1265","DOI":"10.1038\/ejcn.2016.109","volume":"70","author":"BK Ng","year":"2016","unstructured":"Ng, B. K., Hinton, B. J., Fan, B., Kanaya, A. M. & Shepherd, J. A. Clinical anthropometrics and body composition from 3D whole-body surface scans. Eur. J. Clin. Nutr. 70, 1265\u20131270 (2016).","journal-title":"Eur. J. Clin. Nutr."},{"key":"654_CR24","doi-asserted-by":"publisher","first-page":"1316","DOI":"10.1093\/ajcn\/nqz218","volume":"110","author":"BK Ng","year":"2019","unstructured":"Ng, B. K. et al. Detailed 3-dimensional body shape features predict body composition, blood metabolites, and functional strength: the Shape Up! studies. Am. J. Clin. Nutr. 110, 1316\u20131326 (2019).","journal-title":"Am. J. Clin. Nutr."},{"key":"654_CR25","doi-asserted-by":"publisher","first-page":"1795","DOI":"10.1002\/oby.21957","volume":"25","author":"J Sun","year":"2017","unstructured":"Sun, J., Xu, B., Lee, J. & Freeland-Graves, J. H. Novel body shape descriptors for abdominal adiposity prediction using magnetic resonance images and stereovision body images. Obesity 25, 1795\u20131801 (2017).","journal-title":"Obesity"},{"key":"654_CR26","doi-asserted-by":"publisher","first-page":"445","DOI":"10.1002\/ajhb.22663","volume":"27","author":"JJ Lee","year":"2015","unstructured":"Lee, J. J., Freeland-Graves, J. H., Pepper, M. R., Yu, W. & Xu, B. Efficacy of thigh volume ratios assessed via stereovision body imaging as a predictor of visceral adipose tissue measured by magnetic resonance imaging. Am. J. Hum. Biol. J. Hum. Biol. Counc. 27, 445\u2013457 (2015).","journal-title":"Am. J. Hum. Biol. J. Hum. Biol. Counc."},{"key":"654_CR27","first-page":"1729","volume":"2019","author":"Q Wang","year":"2019","unstructured":"Wang, Q., Lu, Y., Zhang, X. & Hahn, J. K. A novel hybrid model for visceral adipose tissue prediction using shape descriptors. Annu. Int Conf. IEEE Eng. Med. Biol. Soc. IEEE Eng. Med. Biol. Soc. 2019, 1729\u20131732 (2019).","journal-title":"Annu. Int Conf. IEEE Eng. Med. Biol. Soc. IEEE Eng. Med. Biol. Soc."},{"key":"654_CR28","doi-asserted-by":"crossref","unstructured":"Majmudar, M. D. et al. Smartphone camera based assessment of adiposity: a validation study. npj Digit. Med. 5, 79 (2022).","DOI":"10.1038\/s41746-022-00628-3"},{"key":"654_CR29","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-020-15948-9","volume":"11","author":"TJ Littlejohns","year":"2020","unstructured":"Littlejohns, T. J. et al. The UK Biobank imaging enhancement of 100,000 participants: rationale, data collection, management and future directions. Nat. Commun. 11, 2624 (2020).","journal-title":"Nat. Commun."},{"key":"654_CR30","doi-asserted-by":"publisher","first-page":"e1001779","DOI":"10.1371\/journal.pmed.1001779","volume":"12","author":"C Sudlow","year":"2015","unstructured":"Sudlow, C. et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, e1001779 (2015).","journal-title":"PLoS Med."},{"key":"654_CR31","doi-asserted-by":"publisher","first-page":"1785","DOI":"10.1002\/oby.22210","volume":"26","author":"J Linge","year":"2018","unstructured":"Linge, J. et al. Body composition profiling in the UK biobank imaging study. Obes. Silver Spring Md 26, 1785\u20131795 (2018).","journal-title":"Obes. Silver Spring Md"},{"key":"654_CR32","doi-asserted-by":"publisher","first-page":"e0163332","DOI":"10.1371\/journal.pone.0163332","volume":"11","author":"J West","year":"2016","unstructured":"West, J. et al. Feasibility of MR-based body composition analysis in large scale population studies. PLoS ONE 11, e0163332 (2016).","journal-title":"PLoS ONE"},{"key":"654_CR33","doi-asserted-by":"publisher","first-page":"1390","DOI":"10.1038\/s41591-019-0563-7","volume":"25","author":"T Karlsson","year":"2019","unstructured":"Karlsson, T. et al. Contribution of genetics to visceral adiposity and its relation to cardiovascular and metabolic disease. Nat. Med. 25, 1390\u20131395 (2019).","journal-title":"Nat. Med."},{"key":"654_CR34","doi-asserted-by":"publisher","first-page":"2622","DOI":"10.1007\/s00125-012-2639-5","volume":"55","author":"BM Kaess","year":"2012","unstructured":"Kaess, B. M. et al. The ratio of visceral to subcutaneous fat, a metric of body fat distribution, is a unique correlate of cardiometabolic risk. Diabetologia 55, 2622\u20132630 (2012).","journal-title":"Diabetologia"},{"key":"654_CR35","doi-asserted-by":"publisher","first-page":"1094","DOI":"10.1038\/oby.2004.137","volume":"12","author":"CI Ardern","year":"2004","unstructured":"Ardern, C. I., Janssen, I., Ross, R. & Katzmarzyk, P. T. Development of health-related waist circumference thresholds within BMI categories. Obes. Res. 12, 1094\u20131103 (2004).","journal-title":"Obes. Res."},{"key":"654_CR36","doi-asserted-by":"publisher","first-page":"680","DOI":"10.1038\/s41430-018-0145-7","volume":"72","author":"SB Heymsfield","year":"2018","unstructured":"Heymsfield, S. B. et al. Digital anthropometry: a critical review. Eur. J. Clin. Nutr. 72, 680\u2013687 (2018).","journal-title":"Eur. J. Clin. Nutr."},{"key":"654_CR37","doi-asserted-by":"publisher","first-page":"1054","DOI":"10.1038\/s41430-019-0526-6","volume":"74","author":"GM Tinsley","year":"2020","unstructured":"Tinsley, G. M., Moore, M. L., Dellinger, J. R., Adamson, B. T. & Benavides, M. L. Digital anthropometry via three-dimensional optical scanning: evaluation of four commercially available systems. Eur. J. Clin. Nutr. 74, 1054\u20131064 (2020).","journal-title":"Eur. J. Clin. Nutr."},{"key":"654_CR38","doi-asserted-by":"publisher","first-page":"3160","DOI":"10.1016\/j.clnu.2020.02.008","volume":"39","author":"GM Tinsley","year":"2020","unstructured":"Tinsley, G. M., Moore, M. L., Benavides, M. L., Dellinger, J. R. & Adamson, B. T. 3-Dimensional optical scanning for body composition assessment: a 4-component model comparison of four commercially available scanners. Clin. Nutr. 39, 3160\u20133167 (2020).","journal-title":"Clin. Nutr."},{"key":"654_CR39","doi-asserted-by":"publisher","first-page":"249","DOI":"10.2337\/db19-0447","volume":"69","author":"C Gonzaga-Jauregui","year":"2020","unstructured":"Gonzaga-Jauregui, C. et al. Clinical and molecular prevalence of lipodystrophy in an unascertained large clinical care cohort. Diabetes 69, 249\u2013258 (2020).","journal-title":"Diabetes"},{"key":"654_CR40","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1038\/72807","volume":"24","author":"S Shackleton","year":"2000","unstructured":"Shackleton, S. et al. LMNA, encoding lamin A\/C, is mutated in partial lipodystrophy. Nat. Genet. 24, 153\u2013156 (2000).","journal-title":"Nat. Genet."},{"key":"654_CR41","doi-asserted-by":"publisher","first-page":"2255","DOI":"10.2337\/dc18-0978","volume":"41","author":"R Meral","year":"2018","unstructured":"Meral, R. et al. \u201cFat shadows\u201d from DXA for the qualitative assessment of lipodystrophy: when a picture is worth a thousand numbers. Diabetes Care 41, 2255\u20132258 (2018).","journal-title":"Diabetes Care"},{"key":"654_CR42","doi-asserted-by":"publisher","first-page":"500","DOI":"10.1007\/s12020-019-01862-8","volume":"64","author":"EA Oral","year":"2019","unstructured":"Oral, E. A. et al. Long-term effectiveness and safety of metreleptin in the treatment of patients with partial lipodystrophy. Endocrine 64, 500\u2013511 (2019).","journal-title":"Endocrine"},{"key":"654_CR43","doi-asserted-by":"publisher","first-page":"3068","DOI":"10.1210\/jc.2018-02787","volume":"104","author":"H Sekizkardes","year":"2019","unstructured":"Sekizkardes, H., Cochran, E., Malandrino, N., Garg, A. & Brown, R. J. Efficacy of metreleptin treatment in familial partial lipodystrophy due to PPARG vs LMNA pathogenic variants. J. Clin. Endocrinol. Metab. 104, 3068\u20133076 (2019).","journal-title":"J. Clin. Endocrinol. Metab."},{"key":"654_CR44","doi-asserted-by":"publisher","first-page":"380","DOI":"10.1001\/jama.2014.8334","volume":"312","author":"TL Stanley","year":"2014","unstructured":"Stanley, T. L. et al. Effect of tesamorelin on visceral fat and liver fat in HIV-infected patients with abdominal fat accumulation: a randomized clinical trial. JAMA 312, 380 (2014).","journal-title":"JAMA"},{"key":"654_CR45","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1038\/ng.3714","volume":"49","author":"LA Lotta","year":"2017","unstructured":"Lotta, L. A. et al. Integrative genomic analysis implicates limited peripheral adipose storage capacity in the pathogenesis of human insulin resistance. Nat. Genet. 49, 17\u201326 (2017).","journal-title":"Nat. Genet"},{"key":"654_CR46","first-page":"907","volume":"101","author":"K Lim","year":"2021","unstructured":"Lim, K., Haider, A., Adams, C., Sleigh, A. & Savage, D. B. Lipodistrophy: a paradigm for understanding the consequences of \u201coverloading\u201d adipose tissue. Physiol. Rev. 101, 907\u2013993 (2021).","journal-title":"Physiol. Rev."},{"key":"654_CR47","doi-asserted-by":"crossref","unstructured":"Agrawal, S. et al. Inherited basis of visceral, abdominal subcutaneous and gluteofemoral fat depots. Nat. Commun. 13, 3771 (2022).","DOI":"10.1038\/s41467-022-30931-2"},{"key":"654_CR48","doi-asserted-by":"publisher","first-page":"186","DOI":"10.1053\/meta.2003.50024","volume":"52","author":"JA Kanaley","year":"2003","unstructured":"Kanaley, J. A., Giannopoulou, I., Tillapaugh-Fay, G., Nappi, J. S. & Ploutz-Snyder, L. L. Racial differences in subcutaneous and visceral fat distribution in postmenopausal black and white women. Metabolism 52, 186\u2013191 (2003).","journal-title":"Metabolism"},{"key":"654_CR49","doi-asserted-by":"publisher","first-page":"5366","DOI":"10.1210\/jcem.86.11.7992","volume":"86","author":"A Raji","year":"2001","unstructured":"Raji, A., Seely, E. W., Arky, R. A. & Simonson, D. C. Body fat distribution and insulin resistance in healthy Asian Indians and Caucasians. J. Clin. Endocrinol. Metab. 86, 5366\u20135371 (2001).","journal-title":"J. Clin. Endocrinol. Metab."},{"key":"654_CR50","doi-asserted-by":"publisher","first-page":"410","DOI":"10.1161\/CIRCULATIONAHA.120.052430","volume":"144","author":"AP Patel","year":"2021","unstructured":"Patel, A. P., Wang, M., Kartoun, U., Ng, K. & Khera, A. V. Quantifying and understanding the higher risk of atherosclerotic cardiovascular disease among South Asian individuals: results from the uk biobank prospective cohort study. Circulation 144, 410\u2013422 (2021).","journal-title":"Circulation"},{"key":"654_CR51","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., van der Maaten, L., Weinberger, K. Q. Densely connected convolutional networks. Preprint at https:\/\/ieeexplore.ieee.org\/document\/8099726 (2018).","DOI":"10.1109\/CVPR.2017.243"},{"key":"654_CR52","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1038\/s41588-021-01011-w","volume":"54","author":"SJ Jurgens","year":"2022","unstructured":"Jurgens, S. J. et al. Analysis of rare genetic variation underlying cardiometabolic diseases and traits among 200,000 individuals in the UK Biobank. Nat. Genet. 54, 240\u2013250 (2022).","journal-title":"Nat. Genet"}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-022-00654-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-022-00654-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-022-00654-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,25]],"date-time":"2022-11-25T03:10:50Z","timestamp":1669345850000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-022-00654-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,27]]},"references-count":52,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["654"],"URL":"https:\/\/doi.org\/10.1038\/s41746-022-00654-1","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2022.01.14.22269328","asserted-by":"object"}]},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,7,27]]},"assertion":[{"value":"18 January 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 July 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 July 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing non-financial interests, but the following competing financial interests: M.D.R.K., N.D., A.P., and P.B. are supported by grants from Bayer AG applying machine learning in cardiovascular disease. S.A. has served as a scientific consultant to Third Rock Ventures. P.T.E. receives sponsored research support from Bayer AG and has consulted for Bayer AG, Novartis, MyoKardia and Quest Diagnostics. A.P. is also employed as a Venture Partner at GV and consulted for Novartis; and has received funding from Intel, Verily and MSFT. K.N. is an employee of IBM Research. P.B serves as a consultant for Novartis. A.V.K. 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.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"105"}}