Association between metabolic risk factors and optic disc cupping identified by deep learning method
This study aims to investigate correlation between metabolic risk factors and optic disc cupping and the development of glaucoma. This study is a retrospective, cross-sectional study with over 20-year-old patients that underwent health screening examinations. Intraocular pressure (IOP), fundus photo...
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description | This study aims to investigate correlation between metabolic risk factors and optic disc cupping and the development of glaucoma. This study is a retrospective, cross-sectional study with over 20-year-old patients that underwent health screening examinations. Intraocular pressure (IOP), fundus photographs, Body Mass Index (BMI), waist circumference (WC), serum triglycerides, serum HDL cholesterol (HDL-C), serum LDL cholesterol (LDL-C), systolic blood pressure (BP), diastolic BP, and serum HbA1c were obtained to analyse correlation between metabolic risk factors and glaucoma. Eye with glaucomatous optic neuropathy(GON) was defined as having an optic disc with either vertical cup-to-disc ratio(VCDR) [greater than or equal to] 0.7 or a VCDR difference [greater than or equal to] 0.2 between the right and left eyes by measuring VCDR with deep learning approach. The study comprised 15,585 subjects and 877 subjects were diagnosed as GON. In univariate analyses, age, BMI, systolic BP, diastolic BP, WC, triglyceride, LDL-C, HbA1c, and IOP were significantly and positively correlated with VCDR in the optic nerve head. In linear regression analysis as independent variables, stepwise multiple regression analyses revealed that age, BMI, systolic BP, HbA1c, and IOP showed positive correlation with VCDR. In multivariate logistic analyses of risk factors and GON, higher age (odds ratio [OR], 1.054; 95% confidence interval [CI], 1.046-1.063), male gender (OR, 0.730; 95% CI, 0.609-0.876), more obese (OR, 1.267; 95% CI, 1.065-1.507), and diabetes (OR, 1.575; 95% CI, 1.214-2.043) remained statistically significant correlation with GON. Among the metabolic risk factors, obesity and diabetes as well as older age and male gender are risk factors of developing GON. The glaucoma screening examinations should be considered in the populations with these indicated risk factors. |
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This study is a retrospective, cross-sectional study with over 20-year-old patients that underwent health screening examinations. Intraocular pressure (IOP), fundus photographs, Body Mass Index (BMI), waist circumference (WC), serum triglycerides, serum HDL cholesterol (HDL-C), serum LDL cholesterol (LDL-C), systolic blood pressure (BP), diastolic BP, and serum HbA1c were obtained to analyse correlation between metabolic risk factors and glaucoma. Eye with glaucomatous optic neuropathy(GON) was defined as having an optic disc with either vertical cup-to-disc ratio(VCDR) [greater than or equal to] 0.7 or a VCDR difference [greater than or equal to] 0.2 between the right and left eyes by measuring VCDR with deep learning approach. The study comprised 15,585 subjects and 877 subjects were diagnosed as GON. In univariate analyses, age, BMI, systolic BP, diastolic BP, WC, triglyceride, LDL-C, HbA1c, and IOP were significantly and positively correlated with VCDR in the optic nerve head. In linear regression analysis as independent variables, stepwise multiple regression analyses revealed that age, BMI, systolic BP, HbA1c, and IOP showed positive correlation with VCDR. In multivariate logistic analyses of risk factors and GON, higher age (odds ratio [OR], 1.054; 95% confidence interval [CI], 1.046-1.063), male gender (OR, 0.730; 95% CI, 0.609-0.876), more obese (OR, 1.267; 95% CI, 1.065-1.507), and diabetes (OR, 1.575; 95% CI, 1.214-2.043) remained statistically significant correlation with GON. Among the metabolic risk factors, obesity and diabetes as well as older age and male gender are risk factors of developing GON. The glaucoma screening examinations should be considered in the populations with these indicated risk factors.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0239071</identifier><identifier>PMID: 32941514</identifier><language>eng</language><publisher>San Francisco: Public Library of Science</publisher><subject>Age ; Biology and Life Sciences ; Blood pressure ; Body mass ; Body mass index ; Body size ; Cholesterol ; Complications and side effects ; Confidence intervals ; Correlation ; Correlation analysis ; Deep learning ; Development and progression ; Diabetes ; Diabetes mellitus ; Diabetic neuropathy ; Glaucoma ; Glucose ; Health aspects ; Health risks ; Hemoglobin ; High density lipoprotein ; Hospitals ; Hypertension ; Independent variables ; Intraocular pressure ; Low density lipoprotein ; Machine learning ; Medical research ; Medical screening ; Medicine ; Medicine and Health Sciences ; Metabolic syndrome ; Metabolic syndrome X ; Neuropathy ; Obesity ; Open-angle glaucoma ; Optic disc ; Optic nerve ; Optic neuropathy ; Pathogenesis ; Regression analysis ; Risk analysis ; Risk factors ; Statistical analysis ; Triglycerides</subject><ispartof>PloS one, 2020-09, Vol.15 (9), p.e0239071-e0239071</ispartof><rights>COPYRIGHT 2020 Public Library of Science</rights><rights>2020 Shin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2020 Shin et al 2020 Shin et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c669t-b04f927b14b935490b8676c618bc8c70037f62f79f9e682f07dab86c32c995013</citedby><cites>FETCH-LOGICAL-c669t-b04f927b14b935490b8676c618bc8c70037f62f79f9e682f07dab86c32c995013</cites><orcidid>0000-0002-1052-2924 ; 0000-0003-1721-1253 ; 0000-0001-5026-4117</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7498045/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7498045/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793,79600,79601</link.rule.ids></links><search><contributor>Bhattacharya, Sanjoy</contributor><creatorcontrib>Shin, Jonghoon</creatorcontrib><creatorcontrib>Kang, Min Seung</creatorcontrib><creatorcontrib>Park, Keunheung</creatorcontrib><creatorcontrib>Lee, Jong Soo</creatorcontrib><creatorcontrib>Bhattacharya, Sanjoy</creatorcontrib><title>Association between metabolic risk factors and optic disc cupping identified by deep learning method</title><title>PloS one</title><description>This study aims to investigate correlation between metabolic risk factors and optic disc cupping and the development of glaucoma. This study is a retrospective, cross-sectional study with over 20-year-old patients that underwent health screening examinations. Intraocular pressure (IOP), fundus photographs, Body Mass Index (BMI), waist circumference (WC), serum triglycerides, serum HDL cholesterol (HDL-C), serum LDL cholesterol (LDL-C), systolic blood pressure (BP), diastolic BP, and serum HbA1c were obtained to analyse correlation between metabolic risk factors and glaucoma. Eye with glaucomatous optic neuropathy(GON) was defined as having an optic disc with either vertical cup-to-disc ratio(VCDR) [greater than or equal to] 0.7 or a VCDR difference [greater than or equal to] 0.2 between the right and left eyes by measuring VCDR with deep learning approach. The study comprised 15,585 subjects and 877 subjects were diagnosed as GON. In univariate analyses, age, BMI, systolic BP, diastolic BP, WC, triglyceride, LDL-C, HbA1c, and IOP were significantly and positively correlated with VCDR in the optic nerve head. In linear regression analysis as independent variables, stepwise multiple regression analyses revealed that age, BMI, systolic BP, HbA1c, and IOP showed positive correlation with VCDR. In multivariate logistic analyses of risk factors and GON, higher age (odds ratio [OR], 1.054; 95% confidence interval [CI], 1.046-1.063), male gender (OR, 0.730; 95% CI, 0.609-0.876), more obese (OR, 1.267; 95% CI, 1.065-1.507), and diabetes (OR, 1.575; 95% CI, 1.214-2.043) remained statistically significant correlation with GON. Among the metabolic risk factors, obesity and diabetes as well as older age and male gender are risk factors of developing GON. The glaucoma screening examinations should be considered in the populations with these indicated risk factors.</description><subject>Age</subject><subject>Biology and Life Sciences</subject><subject>Blood pressure</subject><subject>Body mass</subject><subject>Body mass index</subject><subject>Body size</subject><subject>Cholesterol</subject><subject>Complications and side effects</subject><subject>Confidence intervals</subject><subject>Correlation</subject><subject>Correlation analysis</subject><subject>Deep learning</subject><subject>Development and progression</subject><subject>Diabetes</subject><subject>Diabetes mellitus</subject><subject>Diabetic neuropathy</subject><subject>Glaucoma</subject><subject>Glucose</subject><subject>Health aspects</subject><subject>Health risks</subject><subject>Hemoglobin</subject><subject>High density lipoprotein</subject><subject>Hospitals</subject><subject>Hypertension</subject><subject>Independent variables</subject><subject>Intraocular pressure</subject><subject>Low density lipoprotein</subject><subject>Machine learning</subject><subject>Medical research</subject><subject>Medical screening</subject><subject>Medicine</subject><subject>Medicine and Health Sciences</subject><subject>Metabolic syndrome</subject><subject>Metabolic syndrome X</subject><subject>Neuropathy</subject><subject>Obesity</subject><subject>Open-angle glaucoma</subject><subject>Optic disc</subject><subject>Optic nerve</subject><subject>Optic neuropathy</subject><subject>Pathogenesis</subject><subject>Regression analysis</subject><subject>Risk analysis</subject><subject>Risk factors</subject><subject>Statistical 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between metabolic risk factors and optic disc cupping identified by deep learning method</title><author>Shin, Jonghoon ; Kang, Min Seung ; Park, Keunheung ; Lee, Jong Soo ; Bhattacharya, Sanjoy</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c669t-b04f927b14b935490b8676c618bc8c70037f62f79f9e682f07dab86c32c995013</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Age</topic><topic>Biology and Life Sciences</topic><topic>Blood pressure</topic><topic>Body mass</topic><topic>Body mass index</topic><topic>Body size</topic><topic>Cholesterol</topic><topic>Complications and side effects</topic><topic>Confidence intervals</topic><topic>Correlation</topic><topic>Correlation analysis</topic><topic>Deep learning</topic><topic>Development and progression</topic><topic>Diabetes</topic><topic>Diabetes mellitus</topic><topic>Diabetic 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Soo</au><au>Bhattacharya, Sanjoy</au><au>Bhattacharya, Sanjoy</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Association between metabolic risk factors and optic disc cupping identified by deep learning method</atitle><jtitle>PloS one</jtitle><date>2020-09-17</date><risdate>2020</risdate><volume>15</volume><issue>9</issue><spage>e0239071</spage><epage>e0239071</epage><pages>e0239071-e0239071</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>This study aims to investigate correlation between metabolic risk factors and optic disc cupping and the development of glaucoma. This study is a retrospective, cross-sectional study with over 20-year-old patients that underwent health screening examinations. Intraocular pressure (IOP), fundus photographs, Body Mass Index (BMI), waist circumference (WC), serum triglycerides, serum HDL cholesterol (HDL-C), serum LDL cholesterol (LDL-C), systolic blood pressure (BP), diastolic BP, and serum HbA1c were obtained to analyse correlation between metabolic risk factors and glaucoma. Eye with glaucomatous optic neuropathy(GON) was defined as having an optic disc with either vertical cup-to-disc ratio(VCDR) [greater than or equal to] 0.7 or a VCDR difference [greater than or equal to] 0.2 between the right and left eyes by measuring VCDR with deep learning approach. The study comprised 15,585 subjects and 877 subjects were diagnosed as GON. In univariate analyses, age, BMI, systolic BP, diastolic BP, WC, triglyceride, LDL-C, HbA1c, and IOP were significantly and positively correlated with VCDR in the optic nerve head. In linear regression analysis as independent variables, stepwise multiple regression analyses revealed that age, BMI, systolic BP, HbA1c, and IOP showed positive correlation with VCDR. In multivariate logistic analyses of risk factors and GON, higher age (odds ratio [OR], 1.054; 95% confidence interval [CI], 1.046-1.063), male gender (OR, 0.730; 95% CI, 0.609-0.876), more obese (OR, 1.267; 95% CI, 1.065-1.507), and diabetes (OR, 1.575; 95% CI, 1.214-2.043) remained statistically significant correlation with GON. Among the metabolic risk factors, obesity and diabetes as well as older age and male gender are risk factors of developing GON. The glaucoma screening examinations should be considered in the populations with these indicated risk factors.</abstract><cop>San Francisco</cop><pub>Public Library of Science</pub><pmid>32941514</pmid><doi>10.1371/journal.pone.0239071</doi><tpages>e0239071</tpages><orcidid>https://orcid.org/0000-0002-1052-2924</orcidid><orcidid>https://orcid.org/0000-0003-1721-1253</orcidid><orcidid>https://orcid.org/0000-0001-5026-4117</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Age Biology and Life Sciences Blood pressure Body mass Body mass index Body size Cholesterol Complications and side effects Confidence intervals Correlation Correlation analysis Deep learning Development and progression Diabetes Diabetes mellitus Diabetic neuropathy Glaucoma Glucose Health aspects Health risks Hemoglobin High density lipoprotein Hospitals Hypertension Independent variables Intraocular pressure Low density lipoprotein Machine learning Medical research Medical screening Medicine Medicine and Health Sciences Metabolic syndrome Metabolic syndrome X Neuropathy Obesity Open-angle glaucoma Optic disc Optic nerve Optic neuropathy Pathogenesis Regression analysis Risk analysis Risk factors Statistical analysis Triglycerides |
title | Association between metabolic risk factors and optic disc cupping identified by deep learning method |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-05T10%3A42%3A23IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-gale_plos_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Association%20between%20metabolic%20risk%20factors%20and%20optic%20disc%20cupping%20identified%20by%20deep%20learning%20method&rft.jtitle=PloS%20one&rft.au=Shin,%20Jonghoon&rft.date=2020-09-17&rft.volume=15&rft.issue=9&rft.spage=e0239071&rft.epage=e0239071&rft.pages=e0239071-e0239071&rft.issn=1932-6203&rft.eissn=1932-6203&rft_id=info:doi/10.1371/journal.pone.0239071&rft_dat=%3Cgale_plos_%3EA635747786%3C/gale_plos_%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2443876487&rft_id=info:pmid/32941514&rft_galeid=A635747786&rft_doaj_id=oai_doaj_org_article_33b9609a6f7e4957bbb9c57d760b6b58&rfr_iscdi=true |