Loading…
New insights from continuous glucose monitoring into the route to diabetes
Aim Type 2 diabetes mellitus (T2DM) is preceded by a period of impaired glucoregulation. We investigated if continuous glucose monitoring system (CGMS) (1) could improve our capacity to predict the development of T2DM in subjects at risk. (2) Find out if impaired fasting glucose/impaired glucose tol...
Saved in:
| Published in: | Diabetes/metabolism research and reviews 2018-07, Vol.34 (5), p.e3002-n/a |
|---|---|
| Main Authors: | , , , , |
| 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-c3532-cacca2e19c48464a6742405dab62ca9b5bf4ec7daf63b302afe80ee3294130583 |
|---|---|
| cites | cdi_FETCH-LOGICAL-c3532-cacca2e19c48464a6742405dab62ca9b5bf4ec7daf63b302afe80ee3294130583 |
| container_end_page | n/a |
| container_issue | 5 |
| container_start_page | e3002 |
| container_title | Diabetes/metabolism research and reviews |
| container_volume | 34 |
| creator | Colas, Ana Vigil, Luis Rodríguez de Castro, Carmen Vargas, Borja Varela, Manuel |
| description | Aim
Type 2 diabetes mellitus (T2DM) is preceded by a period of impaired glucoregulation. We investigated if continuous glucose monitoring system (CGMS) (1) could improve our capacity to predict the development of T2DM in subjects at risk. (2) Find out if impaired fasting glucose/impaired glucose tolerance differentiation through CGMS would also elucidate differences in clinical phenotypes.
Material and methods
Observational study of 209 hypertensive patients, aged 18 to 85 years who wore at entry a CGMS. Two CGMS metrics, percent of time under the 100 mg/dL glycaemic threshold (TU100) (impaired fasting glucose surrogate phenotype) and area above the 140 mg/dL glycemic threshold (AO140) (impaired glucose tolerance surrogate phenotype) were measured. The median follow‐up was 32 months (6‐72 mo), and there were 17 new cases of T2DM.
Results
In a multivariate Cox proportional hazard survival analysis including the conventional prediabetes‐defining criteria and the 2 CGMS‐derived variables, only TU100 and HbA1c were significant and independent variables in predicting T2DM development. An increase in 0.1 in TU100 resulted in a 0.69 (95% CI, 0.54‐0.88; P |
| doi_str_mv | 10.1002/dmrr.3002 |
| format | article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2012118865</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2012118865</sourcerecordid><originalsourceid>FETCH-LOGICAL-c3532-cacca2e19c48464a6742405dab62ca9b5bf4ec7daf63b302afe80ee3294130583</originalsourceid><addsrcrecordid>eNp1kEtLw0AUhQdRbK0u_AMScKOLtPPITJKl1DdVoeg6TCY37ZQkU2cSSv-9E1tdCK7uufDxcTgInRM8JhjTSVFbO2Y-HaAh4RSHMRf48DdzOkAnzq0wxiwS0TEa0JQTISgdoudX2AS6cXqxbF1QWlMHyjStbjrTuWBRdco4CGrT6NZY3Sw825qgXUJgTddC4J9CyxxacKfoqJSVg7P9HaGP-7v36WM4e3t4mt7MQsU4o6GSSkkKJFVR4ttIEUc0wryQuaBKpjnPywhUXMhSsJxhKktIMACjaUQY5gkboaudd23NZweuzWrtFFSVbMCXzigmlJAkEdyjl3_Qlels49t5SjDBkjTuhdc7SlnjnIUyW1tdS7vNCM76gbN-4Kwf2LMXe2OX11D8kj-LemCyAza6gu3_puz2ZT7_Vn4BiqCFHA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2063638978</pqid></control><display><type>article</type><title>New insights from continuous glucose monitoring into the route to diabetes</title><source>Wiley</source><creator>Colas, Ana ; Vigil, Luis ; Rodríguez de Castro, Carmen ; Vargas, Borja ; Varela, Manuel</creator><creatorcontrib>Colas, Ana ; Vigil, Luis ; Rodríguez de Castro, Carmen ; Vargas, Borja ; Varela, Manuel</creatorcontrib><description>Aim
Type 2 diabetes mellitus (T2DM) is preceded by a period of impaired glucoregulation. We investigated if continuous glucose monitoring system (CGMS) (1) could improve our capacity to predict the development of T2DM in subjects at risk. (2) Find out if impaired fasting glucose/impaired glucose tolerance differentiation through CGMS would also elucidate differences in clinical phenotypes.
Material and methods
Observational study of 209 hypertensive patients, aged 18 to 85 years who wore at entry a CGMS. Two CGMS metrics, percent of time under the 100 mg/dL glycaemic threshold (TU100) (impaired fasting glucose surrogate phenotype) and area above the 140 mg/dL glycemic threshold (AO140) (impaired glucose tolerance surrogate phenotype) were measured. The median follow‐up was 32 months (6‐72 mo), and there were 17 new cases of T2DM.
Results
In a multivariate Cox proportional hazard survival analysis including the conventional prediabetes‐defining criteria and the 2 CGMS‐derived variables, only TU100 and HbA1c were significant and independent variables in predicting T2DM development. An increase in 0.1 in TU100 resulted in a 0.69 (95% CI, 0.54‐0.88; P < .01) odds ratio of developing T2DM. With cut‐off points of 0.5 for TU100 and 5.7% for HbA1c, the test “TU < 0.5 and HbA1c > 5.7%” had a sensitivity of 0.81 (SD, 0.10), a specificity of 0.83 (SD, 0.03), and a likelihood ratio of 4.82 (SD, 1.03) for T2DM development.
Conclusions
Continuous glucose monitoring system allows for a better T2DM risk‐development categorization than fasting glucose and HbA1c in a high‐risk population. Continuous glucose monitoring system–derived phenotyping reveals clinical differences, not disclosed by conventional fasting plasma glucose/HbA1c categorization. These differences may correlate with distinct pathophysiological mechanisms.
In this observational study of 209 hypertensive patients aged 18–85 years, we investigated if continuous glucose monitoring system (CGMS) could improve our capacity to predict the development of T2DM in patients at risk. Two CGMS metrics, percent of time under the 100‐mg/dL glycaemic threshold (TU100) (impaired fasting glucose surrogate phenotype) and area above the 140‐mg/dL glycaemic threshold (AO140) (impaired glucose tolerance surrogate phenotype), were measured. glycaemic threshold (AO140) (IGT surrogate‐phenotype) were measured. The median follow‐up was 32 months (6–72 months) and there were 17 new cases of T2DM. In a multivariate Cox proportional hazard survival analysis only TU100 and HbA1c were significant and independent variables in predicting T2DM development.</description><identifier>ISSN: 1520-7552</identifier><identifier>EISSN: 1520-7560</identifier><identifier>DOI: 10.1002/dmrr.3002</identifier><identifier>PMID: 29516622</identifier><language>eng</language><publisher>England: Wiley Subscription Services, Inc</publisher><subject>Adolescent ; Adult ; Aged ; Aged, 80 and over ; Biomarkers - blood ; Blood Glucose - analysis ; Blood Glucose Self-Monitoring - methods ; continuous glucose monitoring system ; Diabetes ; Diabetes mellitus ; Diabetes mellitus (non-insulin dependent) ; Diabetes Mellitus, Type 2 - blood ; Diabetes Mellitus, Type 2 - diagnosis ; Diabetes Mellitus, Type 2 - etiology ; diabetes risk development ; Fasting ; Female ; Follow-Up Studies ; Glucose ; Glucose Intolerance - blood ; Glucose Intolerance - diagnosis ; Glucose Intolerance - etiology ; Glucose monitoring ; Glucose tolerance ; Humans ; Hypertension - complications ; impaired fasting glucose ; impaired glucose tolerance ; Laboratory testing ; Male ; Middle Aged ; Phenotypes ; Phenotyping ; Prediabetic State - blood ; Prediabetic State - diagnosis ; Prediabetic State - etiology ; Prognosis ; Survival analysis ; Survival Rate ; Young Adult</subject><ispartof>Diabetes/metabolism research and reviews, 2018-07, Vol.34 (5), p.e3002-n/a</ispartof><rights>Copyright © 2018 John Wiley & Sons, Ltd.</rights><rights>2018 John Wiley & Sons, Ltd.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3532-cacca2e19c48464a6742405dab62ca9b5bf4ec7daf63b302afe80ee3294130583</citedby><cites>FETCH-LOGICAL-c3532-cacca2e19c48464a6742405dab62ca9b5bf4ec7daf63b302afe80ee3294130583</cites><orcidid>0000-0001-7484-2911</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27915,27916</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/29516622$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Colas, Ana</creatorcontrib><creatorcontrib>Vigil, Luis</creatorcontrib><creatorcontrib>Rodríguez de Castro, Carmen</creatorcontrib><creatorcontrib>Vargas, Borja</creatorcontrib><creatorcontrib>Varela, Manuel</creatorcontrib><title>New insights from continuous glucose monitoring into the route to diabetes</title><title>Diabetes/metabolism research and reviews</title><addtitle>Diabetes Metab Res Rev</addtitle><description>Aim
Type 2 diabetes mellitus (T2DM) is preceded by a period of impaired glucoregulation. We investigated if continuous glucose monitoring system (CGMS) (1) could improve our capacity to predict the development of T2DM in subjects at risk. (2) Find out if impaired fasting glucose/impaired glucose tolerance differentiation through CGMS would also elucidate differences in clinical phenotypes.
Material and methods
Observational study of 209 hypertensive patients, aged 18 to 85 years who wore at entry a CGMS. Two CGMS metrics, percent of time under the 100 mg/dL glycaemic threshold (TU100) (impaired fasting glucose surrogate phenotype) and area above the 140 mg/dL glycemic threshold (AO140) (impaired glucose tolerance surrogate phenotype) were measured. The median follow‐up was 32 months (6‐72 mo), and there were 17 new cases of T2DM.
Results
In a multivariate Cox proportional hazard survival analysis including the conventional prediabetes‐defining criteria and the 2 CGMS‐derived variables, only TU100 and HbA1c were significant and independent variables in predicting T2DM development. An increase in 0.1 in TU100 resulted in a 0.69 (95% CI, 0.54‐0.88; P < .01) odds ratio of developing T2DM. With cut‐off points of 0.5 for TU100 and 5.7% for HbA1c, the test “TU < 0.5 and HbA1c > 5.7%” had a sensitivity of 0.81 (SD, 0.10), a specificity of 0.83 (SD, 0.03), and a likelihood ratio of 4.82 (SD, 1.03) for T2DM development.
Conclusions
Continuous glucose monitoring system allows for a better T2DM risk‐development categorization than fasting glucose and HbA1c in a high‐risk population. Continuous glucose monitoring system–derived phenotyping reveals clinical differences, not disclosed by conventional fasting plasma glucose/HbA1c categorization. These differences may correlate with distinct pathophysiological mechanisms.
In this observational study of 209 hypertensive patients aged 18–85 years, we investigated if continuous glucose monitoring system (CGMS) could improve our capacity to predict the development of T2DM in patients at risk. Two CGMS metrics, percent of time under the 100‐mg/dL glycaemic threshold (TU100) (impaired fasting glucose surrogate phenotype) and area above the 140‐mg/dL glycaemic threshold (AO140) (impaired glucose tolerance surrogate phenotype), were measured. glycaemic threshold (AO140) (IGT surrogate‐phenotype) were measured. The median follow‐up was 32 months (6–72 months) and there were 17 new cases of T2DM. In a multivariate Cox proportional hazard survival analysis only TU100 and HbA1c were significant and independent variables in predicting T2DM development.</description><subject>Adolescent</subject><subject>Adult</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Biomarkers - blood</subject><subject>Blood Glucose - analysis</subject><subject>Blood Glucose Self-Monitoring - methods</subject><subject>continuous glucose monitoring system</subject><subject>Diabetes</subject><subject>Diabetes mellitus</subject><subject>Diabetes mellitus (non-insulin dependent)</subject><subject>Diabetes Mellitus, Type 2 - blood</subject><subject>Diabetes Mellitus, Type 2 - diagnosis</subject><subject>Diabetes Mellitus, Type 2 - etiology</subject><subject>diabetes risk development</subject><subject>Fasting</subject><subject>Female</subject><subject>Follow-Up Studies</subject><subject>Glucose</subject><subject>Glucose Intolerance - blood</subject><subject>Glucose Intolerance - diagnosis</subject><subject>Glucose Intolerance - etiology</subject><subject>Glucose monitoring</subject><subject>Glucose tolerance</subject><subject>Humans</subject><subject>Hypertension - complications</subject><subject>impaired fasting glucose</subject><subject>impaired glucose tolerance</subject><subject>Laboratory testing</subject><subject>Male</subject><subject>Middle Aged</subject><subject>Phenotypes</subject><subject>Phenotyping</subject><subject>Prediabetic State - blood</subject><subject>Prediabetic State - diagnosis</subject><subject>Prediabetic State - etiology</subject><subject>Prognosis</subject><subject>Survival analysis</subject><subject>Survival Rate</subject><subject>Young Adult</subject><issn>1520-7552</issn><issn>1520-7560</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><recordid>eNp1kEtLw0AUhQdRbK0u_AMScKOLtPPITJKl1DdVoeg6TCY37ZQkU2cSSv-9E1tdCK7uufDxcTgInRM8JhjTSVFbO2Y-HaAh4RSHMRf48DdzOkAnzq0wxiwS0TEa0JQTISgdoudX2AS6cXqxbF1QWlMHyjStbjrTuWBRdco4CGrT6NZY3Sw825qgXUJgTddC4J9CyxxacKfoqJSVg7P9HaGP-7v36WM4e3t4mt7MQsU4o6GSSkkKJFVR4ttIEUc0wryQuaBKpjnPywhUXMhSsJxhKktIMACjaUQY5gkboaudd23NZweuzWrtFFSVbMCXzigmlJAkEdyjl3_Qlels49t5SjDBkjTuhdc7SlnjnIUyW1tdS7vNCM76gbN-4Kwf2LMXe2OX11D8kj-LemCyAza6gu3_puz2ZT7_Vn4BiqCFHA</recordid><startdate>201807</startdate><enddate>201807</enddate><creator>Colas, Ana</creator><creator>Vigil, Luis</creator><creator>Rodríguez de Castro, Carmen</creator><creator>Vargas, Borja</creator><creator>Varela, Manuel</creator><general>Wiley Subscription Services, Inc</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7T5</scope><scope>7TK</scope><scope>H94</scope><scope>K9.</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0001-7484-2911</orcidid></search><sort><creationdate>201807</creationdate><title>New insights from continuous glucose monitoring into the route to diabetes</title><author>Colas, Ana ; Vigil, Luis ; Rodríguez de Castro, Carmen ; Vargas, Borja ; Varela, Manuel</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3532-cacca2e19c48464a6742405dab62ca9b5bf4ec7daf63b302afe80ee3294130583</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Adolescent</topic><topic>Adult</topic><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Biomarkers - blood</topic><topic>Blood Glucose - analysis</topic><topic>Blood Glucose Self-Monitoring - methods</topic><topic>continuous glucose monitoring system</topic><topic>Diabetes</topic><topic>Diabetes mellitus</topic><topic>Diabetes mellitus (non-insulin dependent)</topic><topic>Diabetes Mellitus, Type 2 - blood</topic><topic>Diabetes Mellitus, Type 2 - diagnosis</topic><topic>Diabetes Mellitus, Type 2 - etiology</topic><topic>diabetes risk development</topic><topic>Fasting</topic><topic>Female</topic><topic>Follow-Up Studies</topic><topic>Glucose</topic><topic>Glucose Intolerance - blood</topic><topic>Glucose Intolerance - diagnosis</topic><topic>Glucose Intolerance - etiology</topic><topic>Glucose monitoring</topic><topic>Glucose tolerance</topic><topic>Humans</topic><topic>Hypertension - complications</topic><topic>impaired fasting glucose</topic><topic>impaired glucose tolerance</topic><topic>Laboratory testing</topic><topic>Male</topic><topic>Middle Aged</topic><topic>Phenotypes</topic><topic>Phenotyping</topic><topic>Prediabetic State - blood</topic><topic>Prediabetic State - diagnosis</topic><topic>Prediabetic State - etiology</topic><topic>Prognosis</topic><topic>Survival analysis</topic><topic>Survival Rate</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Colas, Ana</creatorcontrib><creatorcontrib>Vigil, Luis</creatorcontrib><creatorcontrib>Rodríguez de Castro, Carmen</creatorcontrib><creatorcontrib>Vargas, Borja</creatorcontrib><creatorcontrib>Varela, Manuel</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Immunology Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>MEDLINE - Academic</collection><jtitle>Diabetes/metabolism research and reviews</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Colas, Ana</au><au>Vigil, Luis</au><au>Rodríguez de Castro, Carmen</au><au>Vargas, Borja</au><au>Varela, Manuel</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>New insights from continuous glucose monitoring into the route to diabetes</atitle><jtitle>Diabetes/metabolism research and reviews</jtitle><addtitle>Diabetes Metab Res Rev</addtitle><date>2018-07</date><risdate>2018</risdate><volume>34</volume><issue>5</issue><spage>e3002</spage><epage>n/a</epage><pages>e3002-n/a</pages><issn>1520-7552</issn><eissn>1520-7560</eissn><abstract>Aim
Type 2 diabetes mellitus (T2DM) is preceded by a period of impaired glucoregulation. We investigated if continuous glucose monitoring system (CGMS) (1) could improve our capacity to predict the development of T2DM in subjects at risk. (2) Find out if impaired fasting glucose/impaired glucose tolerance differentiation through CGMS would also elucidate differences in clinical phenotypes.
Material and methods
Observational study of 209 hypertensive patients, aged 18 to 85 years who wore at entry a CGMS. Two CGMS metrics, percent of time under the 100 mg/dL glycaemic threshold (TU100) (impaired fasting glucose surrogate phenotype) and area above the 140 mg/dL glycemic threshold (AO140) (impaired glucose tolerance surrogate phenotype) were measured. The median follow‐up was 32 months (6‐72 mo), and there were 17 new cases of T2DM.
Results
In a multivariate Cox proportional hazard survival analysis including the conventional prediabetes‐defining criteria and the 2 CGMS‐derived variables, only TU100 and HbA1c were significant and independent variables in predicting T2DM development. An increase in 0.1 in TU100 resulted in a 0.69 (95% CI, 0.54‐0.88; P < .01) odds ratio of developing T2DM. With cut‐off points of 0.5 for TU100 and 5.7% for HbA1c, the test “TU < 0.5 and HbA1c > 5.7%” had a sensitivity of 0.81 (SD, 0.10), a specificity of 0.83 (SD, 0.03), and a likelihood ratio of 4.82 (SD, 1.03) for T2DM development.
Conclusions
Continuous glucose monitoring system allows for a better T2DM risk‐development categorization than fasting glucose and HbA1c in a high‐risk population. Continuous glucose monitoring system–derived phenotyping reveals clinical differences, not disclosed by conventional fasting plasma glucose/HbA1c categorization. These differences may correlate with distinct pathophysiological mechanisms.
In this observational study of 209 hypertensive patients aged 18–85 years, we investigated if continuous glucose monitoring system (CGMS) could improve our capacity to predict the development of T2DM in patients at risk. Two CGMS metrics, percent of time under the 100‐mg/dL glycaemic threshold (TU100) (impaired fasting glucose surrogate phenotype) and area above the 140‐mg/dL glycaemic threshold (AO140) (impaired glucose tolerance surrogate phenotype), were measured. glycaemic threshold (AO140) (IGT surrogate‐phenotype) were measured. The median follow‐up was 32 months (6–72 months) and there were 17 new cases of T2DM. In a multivariate Cox proportional hazard survival analysis only TU100 and HbA1c were significant and independent variables in predicting T2DM development.</abstract><cop>England</cop><pub>Wiley Subscription Services, Inc</pub><pmid>29516622</pmid><doi>10.1002/dmrr.3002</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0001-7484-2911</orcidid></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1520-7552 |
| ispartof | Diabetes/metabolism research and reviews, 2018-07, Vol.34 (5), p.e3002-n/a |
| issn | 1520-7552 1520-7560 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_2012118865 |
| source | Wiley |
| subjects | Adolescent Adult Aged Aged, 80 and over Biomarkers - blood Blood Glucose - analysis Blood Glucose Self-Monitoring - methods continuous glucose monitoring system Diabetes Diabetes mellitus Diabetes mellitus (non-insulin dependent) Diabetes Mellitus, Type 2 - blood Diabetes Mellitus, Type 2 - diagnosis Diabetes Mellitus, Type 2 - etiology diabetes risk development Fasting Female Follow-Up Studies Glucose Glucose Intolerance - blood Glucose Intolerance - diagnosis Glucose Intolerance - etiology Glucose monitoring Glucose tolerance Humans Hypertension - complications impaired fasting glucose impaired glucose tolerance Laboratory testing Male Middle Aged Phenotypes Phenotyping Prediabetic State - blood Prediabetic State - diagnosis Prediabetic State - etiology Prognosis Survival analysis Survival Rate Young Adult |
| title | New insights from continuous glucose monitoring into the route to diabetes |
| url | http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-15T06%3A39%3A56IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=New%20insights%20from%20continuous%20glucose%20monitoring%20into%20the%20route%20to%20diabetes&rft.jtitle=Diabetes/metabolism%20research%20and%20reviews&rft.au=Colas,%20Ana&rft.date=2018-07&rft.volume=34&rft.issue=5&rft.spage=e3002&rft.epage=n/a&rft.pages=e3002-n/a&rft.issn=1520-7552&rft.eissn=1520-7560&rft_id=info:doi/10.1002/dmrr.3002&rft_dat=%3Cproquest_cross%3E2012118865%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c3532-cacca2e19c48464a6742405dab62ca9b5bf4ec7daf63b302afe80ee3294130583%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2063638978&rft_id=info:pmid/29516622&rfr_iscdi=true |