Loading…

Lipidomic Signatures of Changes in Adiposity: A Large Prospective Study of 5849 Adults from the Australian Diabetes, Obesity and Lifestyle Study

Lipid metabolism is tightly linked to adiposity. Comprehensive lipidomic profiling offers new insights into the dysregulation of lipid metabolism in relation to weight gain. Here, we investigated the relationship of the human plasma lipidome and changes in waist circumference (WC) and body mass inde...

Full description

Saved in:
Bibliographic Details
Published in:Metabolites 2021-09, Vol.11 (9), p.646
Main Authors: Beyene, Habtamu B., Olshansky, Gavriel, Giles, Corey, Huynh, Kevin, Cinel, Michelle, Mellett, Natalie A., Smith, Adam Alexander T., Shaw, Jonathan E., Magliano, Dianna J., Meikle, Peter J.
Format: Article
Language:English
Subjects:
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c461t-575ebdcb1baafe5bcd94db3a4c31d79405bc8af4c927a0ed665aaf487203d1c03
cites cdi_FETCH-LOGICAL-c461t-575ebdcb1baafe5bcd94db3a4c31d79405bc8af4c927a0ed665aaf487203d1c03
container_end_page
container_issue 9
container_start_page 646
container_title Metabolites
container_volume 11
creator Beyene, Habtamu B.
Olshansky, Gavriel
Giles, Corey
Huynh, Kevin
Cinel, Michelle
Mellett, Natalie A.
Smith, Adam Alexander T.
Shaw, Jonathan E.
Magliano, Dianna J.
Meikle, Peter J.
description Lipid metabolism is tightly linked to adiposity. Comprehensive lipidomic profiling offers new insights into the dysregulation of lipid metabolism in relation to weight gain. Here, we investigated the relationship of the human plasma lipidome and changes in waist circumference (WC) and body mass index (BMI). Adults (2653 men and 3196 women), 25–95 years old who attended the baseline survey of the Australian Diabetes, Obesity and Lifestyle Study (AusDiab) and the 5-year follow-up were enrolled. A targeted lipidomic approach was used to quantify 706 distinct molecular lipid species in the plasma samples. Multiple linear regression models were used to examine the relationship between the baseline lipidomic profile and changes in WC and BMI. Metabolic scores for change in WC were generated using a ridge regression model. Alkyl-diacylglycerol such as TG(O-50:2) [NL-18:1] displayed the strongest association with change in WC (β-coefficient = 0.125 cm increment per SD increment in baseline lipid level, p = 2.78 × 10−11. Many lipid species containing linoleate (18:2) fatty acids were negatively associated with both WC and BMI gain. Compared to traditional models, multivariate models containing lipid species identify individuals at a greater risk of gaining WC: top quintile relative to bottom quintile (odds ratio, 95% CI = 5.4, 3.8–6.6 for women and 2.3, 1.7–3.0 for men). Our findings define metabolic profiles that characterize individuals at risk of weight gain or WC increase and provide important insight into the biological role of lipids in obesity.
doi_str_mv 10.3390/metabo11090646
format article
fullrecord <record><control><sourceid>proquest_doaj_</sourceid><recordid>TN_cdi_doaj_primary_oai_doaj_org_article_d59a4998a48942b490fe5056ac25e8ae</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><doaj_id>oai_doaj_org_article_d59a4998a48942b490fe5056ac25e8ae</doaj_id><sourcerecordid>2576437030</sourcerecordid><originalsourceid>FETCH-LOGICAL-c461t-575ebdcb1baafe5bcd94db3a4c31d79405bc8af4c927a0ed665aaf487203d1c03</originalsourceid><addsrcrecordid>eNpdkktvEzEQgFcIRKvSK2dLXDiQYq8fu-aAFIVXpUhFKpytWXs2cbS7Dra3Uv4FPxmniRDFF1vjz58946mq14zecK7p-xEzdIExqqkS6ll1WdesXTDd6uf_rC-q65R2tAxFZUPZy-qCC6mEUPVl9Xvt996F0Vty7zcT5DliIqEnqy1Mm7L0E1k6vw_J58MHsiRriBsk32NIe7TZPyC5z7M7HI_IVugCz0NOpI9hJHmLZDmnHGHwMJFPHjrMmN6Ruw6PPgKTI2vfY8qH4Sx6Vb3oYUh4fZ6vqp9fPv9YfVus777erpbrhRWK5YVsJHbOdqwD6FF21mnhOg7CcuYaLWgJtdALq-sGKDqlZAFF29SUO2Ypv6puT14XYGf20Y8QDyaAN4-BEDcGYvZ2QOOkBqF1C6LVou6EpuVGKhXYWmILWFwfT6793I3oLE7HlJ9In-5Mfms24cG0omG8ZUXw9iyI4ddcymFGnywOA0wY5mRq2SjNlNJ1Qd_8h-7CHKdSqkdK8IbyY3Y3J8qWj0oR-7-PYdQce8c87R3-B-GOuFE</addsrcrecordid><sourcetype>Open Website</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2576437030</pqid></control><display><type>article</type><title>Lipidomic Signatures of Changes in Adiposity: A Large Prospective Study of 5849 Adults from the Australian Diabetes, Obesity and Lifestyle Study</title><source>Open Access: PubMed Central</source><source>Publicly Available Content (ProQuest)</source><creator>Beyene, Habtamu B. ; Olshansky, Gavriel ; Giles, Corey ; Huynh, Kevin ; Cinel, Michelle ; Mellett, Natalie A. ; Smith, Adam Alexander T. ; Shaw, Jonathan E. ; Magliano, Dianna J. ; Meikle, Peter J.</creator><creatorcontrib>Beyene, Habtamu B. ; Olshansky, Gavriel ; Giles, Corey ; Huynh, Kevin ; Cinel, Michelle ; Mellett, Natalie A. ; Smith, Adam Alexander T. ; Shaw, Jonathan E. ; Magliano, Dianna J. ; Meikle, Peter J.</creatorcontrib><description>Lipid metabolism is tightly linked to adiposity. Comprehensive lipidomic profiling offers new insights into the dysregulation of lipid metabolism in relation to weight gain. Here, we investigated the relationship of the human plasma lipidome and changes in waist circumference (WC) and body mass index (BMI). Adults (2653 men and 3196 women), 25–95 years old who attended the baseline survey of the Australian Diabetes, Obesity and Lifestyle Study (AusDiab) and the 5-year follow-up were enrolled. A targeted lipidomic approach was used to quantify 706 distinct molecular lipid species in the plasma samples. Multiple linear regression models were used to examine the relationship between the baseline lipidomic profile and changes in WC and BMI. Metabolic scores for change in WC were generated using a ridge regression model. Alkyl-diacylglycerol such as TG(O-50:2) [NL-18:1] displayed the strongest association with change in WC (β-coefficient = 0.125 cm increment per SD increment in baseline lipid level, p = 2.78 × 10−11. Many lipid species containing linoleate (18:2) fatty acids were negatively associated with both WC and BMI gain. Compared to traditional models, multivariate models containing lipid species identify individuals at a greater risk of gaining WC: top quintile relative to bottom quintile (odds ratio, 95% CI = 5.4, 3.8–6.6 for women and 2.3, 1.7–3.0 for men). Our findings define metabolic profiles that characterize individuals at risk of weight gain or WC increase and provide important insight into the biological role of lipids in obesity.</description><identifier>ISSN: 2218-1989</identifier><identifier>EISSN: 2218-1989</identifier><identifier>DOI: 10.3390/metabo11090646</identifier><identifier>PMID: 34564462</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Adipose tissue ; Age ; Biomarkers ; Body mass index ; Body weight gain ; change in BMI ; change in WC ; Cholesterol ; Diabetes ; Diabetes mellitus ; Diglycerides ; Fatty acids ; Lipid metabolism ; Lipids ; metabolic scores ; Metabolism ; Metabolites ; NMR ; Nuclear magnetic resonance ; Obesity ; plasma lipidomics ; Population ; Regression analysis ; Species ; Triglycerides ; Weight control</subject><ispartof>Metabolites, 2021-09, Vol.11 (9), p.646</ispartof><rights>2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2021 by the authors. 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c461t-575ebdcb1baafe5bcd94db3a4c31d79405bc8af4c927a0ed665aaf487203d1c03</citedby><cites>FETCH-LOGICAL-c461t-575ebdcb1baafe5bcd94db3a4c31d79405bc8af4c927a0ed665aaf487203d1c03</cites><orcidid>0000-0001-6170-2207 ; 0000-0003-4593-3042 ; 0000-0002-6050-1259 ; 0000-0002-2593-4665</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2576437030/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2576437030?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25744,27915,27916,37003,37004,44581,53782,53784,74887</link.rule.ids></links><search><creatorcontrib>Beyene, Habtamu B.</creatorcontrib><creatorcontrib>Olshansky, Gavriel</creatorcontrib><creatorcontrib>Giles, Corey</creatorcontrib><creatorcontrib>Huynh, Kevin</creatorcontrib><creatorcontrib>Cinel, Michelle</creatorcontrib><creatorcontrib>Mellett, Natalie A.</creatorcontrib><creatorcontrib>Smith, Adam Alexander T.</creatorcontrib><creatorcontrib>Shaw, Jonathan E.</creatorcontrib><creatorcontrib>Magliano, Dianna J.</creatorcontrib><creatorcontrib>Meikle, Peter J.</creatorcontrib><title>Lipidomic Signatures of Changes in Adiposity: A Large Prospective Study of 5849 Adults from the Australian Diabetes, Obesity and Lifestyle Study</title><title>Metabolites</title><description>Lipid metabolism is tightly linked to adiposity. Comprehensive lipidomic profiling offers new insights into the dysregulation of lipid metabolism in relation to weight gain. Here, we investigated the relationship of the human plasma lipidome and changes in waist circumference (WC) and body mass index (BMI). Adults (2653 men and 3196 women), 25–95 years old who attended the baseline survey of the Australian Diabetes, Obesity and Lifestyle Study (AusDiab) and the 5-year follow-up were enrolled. A targeted lipidomic approach was used to quantify 706 distinct molecular lipid species in the plasma samples. Multiple linear regression models were used to examine the relationship between the baseline lipidomic profile and changes in WC and BMI. Metabolic scores for change in WC were generated using a ridge regression model. Alkyl-diacylglycerol such as TG(O-50:2) [NL-18:1] displayed the strongest association with change in WC (β-coefficient = 0.125 cm increment per SD increment in baseline lipid level, p = 2.78 × 10−11. Many lipid species containing linoleate (18:2) fatty acids were negatively associated with both WC and BMI gain. Compared to traditional models, multivariate models containing lipid species identify individuals at a greater risk of gaining WC: top quintile relative to bottom quintile (odds ratio, 95% CI = 5.4, 3.8–6.6 for women and 2.3, 1.7–3.0 for men). Our findings define metabolic profiles that characterize individuals at risk of weight gain or WC increase and provide important insight into the biological role of lipids in obesity.</description><subject>Adipose tissue</subject><subject>Age</subject><subject>Biomarkers</subject><subject>Body mass index</subject><subject>Body weight gain</subject><subject>change in BMI</subject><subject>change in WC</subject><subject>Cholesterol</subject><subject>Diabetes</subject><subject>Diabetes mellitus</subject><subject>Diglycerides</subject><subject>Fatty acids</subject><subject>Lipid metabolism</subject><subject>Lipids</subject><subject>metabolic scores</subject><subject>Metabolism</subject><subject>Metabolites</subject><subject>NMR</subject><subject>Nuclear magnetic resonance</subject><subject>Obesity</subject><subject>plasma lipidomics</subject><subject>Population</subject><subject>Regression analysis</subject><subject>Species</subject><subject>Triglycerides</subject><subject>Weight control</subject><issn>2218-1989</issn><issn>2218-1989</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNpdkktvEzEQgFcIRKvSK2dLXDiQYq8fu-aAFIVXpUhFKpytWXs2cbS7Dra3Uv4FPxmniRDFF1vjz58946mq14zecK7p-xEzdIExqqkS6ll1WdesXTDd6uf_rC-q65R2tAxFZUPZy-qCC6mEUPVl9Xvt996F0Vty7zcT5DliIqEnqy1Mm7L0E1k6vw_J58MHsiRriBsk32NIe7TZPyC5z7M7HI_IVugCz0NOpI9hJHmLZDmnHGHwMJFPHjrMmN6Ruw6PPgKTI2vfY8qH4Sx6Vb3oYUh4fZ6vqp9fPv9YfVus777erpbrhRWK5YVsJHbOdqwD6FF21mnhOg7CcuYaLWgJtdALq-sGKDqlZAFF29SUO2Ypv6puT14XYGf20Y8QDyaAN4-BEDcGYvZ2QOOkBqF1C6LVou6EpuVGKhXYWmILWFwfT6793I3oLE7HlJ9In-5Mfms24cG0omG8ZUXw9iyI4ddcymFGnywOA0wY5mRq2SjNlNJ1Qd_8h-7CHKdSqkdK8IbyY3Y3J8qWj0oR-7-PYdQce8c87R3-B-GOuFE</recordid><startdate>20210921</startdate><enddate>20210921</enddate><creator>Beyene, Habtamu B.</creator><creator>Olshansky, Gavriel</creator><creator>Giles, Corey</creator><creator>Huynh, Kevin</creator><creator>Cinel, Michelle</creator><creator>Mellett, Natalie A.</creator><creator>Smith, Adam Alexander T.</creator><creator>Shaw, Jonathan E.</creator><creator>Magliano, Dianna J.</creator><creator>Meikle, Peter J.</creator><general>MDPI AG</general><general>MDPI</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7QR</scope><scope>8FD</scope><scope>8FE</scope><scope>8FH</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>LK8</scope><scope>M7P</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0001-6170-2207</orcidid><orcidid>https://orcid.org/0000-0003-4593-3042</orcidid><orcidid>https://orcid.org/0000-0002-6050-1259</orcidid><orcidid>https://orcid.org/0000-0002-2593-4665</orcidid></search><sort><creationdate>20210921</creationdate><title>Lipidomic Signatures of Changes in Adiposity: A Large Prospective Study of 5849 Adults from the Australian Diabetes, Obesity and Lifestyle Study</title><author>Beyene, Habtamu B. ; Olshansky, Gavriel ; Giles, Corey ; Huynh, Kevin ; Cinel, Michelle ; Mellett, Natalie A. ; Smith, Adam Alexander T. ; Shaw, Jonathan E. ; Magliano, Dianna J. ; Meikle, Peter J.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c461t-575ebdcb1baafe5bcd94db3a4c31d79405bc8af4c927a0ed665aaf487203d1c03</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Adipose tissue</topic><topic>Age</topic><topic>Biomarkers</topic><topic>Body mass index</topic><topic>Body weight gain</topic><topic>change in BMI</topic><topic>change in WC</topic><topic>Cholesterol</topic><topic>Diabetes</topic><topic>Diabetes mellitus</topic><topic>Diglycerides</topic><topic>Fatty acids</topic><topic>Lipid metabolism</topic><topic>Lipids</topic><topic>metabolic scores</topic><topic>Metabolism</topic><topic>Metabolites</topic><topic>NMR</topic><topic>Nuclear magnetic resonance</topic><topic>Obesity</topic><topic>plasma lipidomics</topic><topic>Population</topic><topic>Regression analysis</topic><topic>Species</topic><topic>Triglycerides</topic><topic>Weight control</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Beyene, Habtamu B.</creatorcontrib><creatorcontrib>Olshansky, Gavriel</creatorcontrib><creatorcontrib>Giles, Corey</creatorcontrib><creatorcontrib>Huynh, Kevin</creatorcontrib><creatorcontrib>Cinel, Michelle</creatorcontrib><creatorcontrib>Mellett, Natalie A.</creatorcontrib><creatorcontrib>Smith, Adam Alexander T.</creatorcontrib><creatorcontrib>Shaw, Jonathan E.</creatorcontrib><creatorcontrib>Magliano, Dianna J.</creatorcontrib><creatorcontrib>Meikle, Peter J.</creatorcontrib><collection>CrossRef</collection><collection>Chemoreception Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>ProQuest Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Biological Science Collection</collection><collection>ProQuest Biological Science Journals</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Publicly Available Content (ProQuest)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ: Directory of Open Access Journals</collection><jtitle>Metabolites</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Beyene, Habtamu B.</au><au>Olshansky, Gavriel</au><au>Giles, Corey</au><au>Huynh, Kevin</au><au>Cinel, Michelle</au><au>Mellett, Natalie A.</au><au>Smith, Adam Alexander T.</au><au>Shaw, Jonathan E.</au><au>Magliano, Dianna J.</au><au>Meikle, Peter J.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Lipidomic Signatures of Changes in Adiposity: A Large Prospective Study of 5849 Adults from the Australian Diabetes, Obesity and Lifestyle Study</atitle><jtitle>Metabolites</jtitle><date>2021-09-21</date><risdate>2021</risdate><volume>11</volume><issue>9</issue><spage>646</spage><pages>646-</pages><issn>2218-1989</issn><eissn>2218-1989</eissn><abstract>Lipid metabolism is tightly linked to adiposity. Comprehensive lipidomic profiling offers new insights into the dysregulation of lipid metabolism in relation to weight gain. Here, we investigated the relationship of the human plasma lipidome and changes in waist circumference (WC) and body mass index (BMI). Adults (2653 men and 3196 women), 25–95 years old who attended the baseline survey of the Australian Diabetes, Obesity and Lifestyle Study (AusDiab) and the 5-year follow-up were enrolled. A targeted lipidomic approach was used to quantify 706 distinct molecular lipid species in the plasma samples. Multiple linear regression models were used to examine the relationship between the baseline lipidomic profile and changes in WC and BMI. Metabolic scores for change in WC were generated using a ridge regression model. Alkyl-diacylglycerol such as TG(O-50:2) [NL-18:1] displayed the strongest association with change in WC (β-coefficient = 0.125 cm increment per SD increment in baseline lipid level, p = 2.78 × 10−11. Many lipid species containing linoleate (18:2) fatty acids were negatively associated with both WC and BMI gain. Compared to traditional models, multivariate models containing lipid species identify individuals at a greater risk of gaining WC: top quintile relative to bottom quintile (odds ratio, 95% CI = 5.4, 3.8–6.6 for women and 2.3, 1.7–3.0 for men). Our findings define metabolic profiles that characterize individuals at risk of weight gain or WC increase and provide important insight into the biological role of lipids in obesity.</abstract><cop>Basel</cop><pub>MDPI AG</pub><pmid>34564462</pmid><doi>10.3390/metabo11090646</doi><orcidid>https://orcid.org/0000-0001-6170-2207</orcidid><orcidid>https://orcid.org/0000-0003-4593-3042</orcidid><orcidid>https://orcid.org/0000-0002-6050-1259</orcidid><orcidid>https://orcid.org/0000-0002-2593-4665</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 2218-1989
ispartof Metabolites, 2021-09, Vol.11 (9), p.646
issn 2218-1989
2218-1989
language eng
recordid cdi_doaj_primary_oai_doaj_org_article_d59a4998a48942b490fe5056ac25e8ae
source Open Access: PubMed Central; Publicly Available Content (ProQuest)
subjects Adipose tissue
Age
Biomarkers
Body mass index
Body weight gain
change in BMI
change in WC
Cholesterol
Diabetes
Diabetes mellitus
Diglycerides
Fatty acids
Lipid metabolism
Lipids
metabolic scores
Metabolism
Metabolites
NMR
Nuclear magnetic resonance
Obesity
plasma lipidomics
Population
Regression analysis
Species
Triglycerides
Weight control
title Lipidomic Signatures of Changes in Adiposity: A Large Prospective Study of 5849 Adults from the Australian Diabetes, Obesity and Lifestyle Study
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-14T23%3A20%3A02IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_doaj_&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Lipidomic%20Signatures%20of%20Changes%20in%20Adiposity:%20A%20Large%20Prospective%20Study%20of%205849%20Adults%20from%20the%20Australian%20Diabetes,%20Obesity%20and%20Lifestyle%20Study&rft.jtitle=Metabolites&rft.au=Beyene,%20Habtamu%20B.&rft.date=2021-09-21&rft.volume=11&rft.issue=9&rft.spage=646&rft.pages=646-&rft.issn=2218-1989&rft.eissn=2218-1989&rft_id=info:doi/10.3390/metabo11090646&rft_dat=%3Cproquest_doaj_%3E2576437030%3C/proquest_doaj_%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c461t-575ebdcb1baafe5bcd94db3a4c31d79405bc8af4c927a0ed665aaf487203d1c03%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2576437030&rft_id=info:pmid/34564462&rfr_iscdi=true