Longitudinal Change in Fasting Blood Glucose and Myocardial Infarction Risk in a Population Without Diabetes
To examine the change in fasting blood glucose (FBG) during repeated assessments over time and its potential impact on the risk of developing myocardial infarction (MI). This prospective cohort study included 68,297 participants without diabetes (mean age 49 years) who were free of MI, stroke, and c...
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Veröffentlicht in: | Diabetes care 2017-11, Vol.40 (11), p.1565-1572 |
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creator | Jin, Cheng Chen, Shuohua Vaidya, Anand Wu, Yuntao Wu, Zhijun Hu, Frank B Kris-Etherton, Penny Wu, Shouling Gao, Xiang |
description | To examine the change in fasting blood glucose (FBG) during repeated assessments over time and its potential impact on the risk of developing myocardial infarction (MI).
This prospective cohort study included 68,297 participants without diabetes (mean age 49 years) who were free of MI, stroke, and cancer prior to or in 2010 (baseline of the current analysis). FBG concentrations were measured in 2006, 2008, and 2010. The FBG trajectories during 2006-2010, the primary exposure of the current study, were identified by latent mixture modeling. Incident MI cases were confirmed via review of medical records by cardiologists.
We identified five discrete FBG trajectories according to FBG range and changing pattern over time: elevated-stable (
= 3,877), elevated-decreasing (
= 7,060), moderate-increasing (
= 10,298), moderate-stable (
= 40,352), and low-stable (
= 6,710). During 4 years of follow-up, we documented 283 incident MI cases. Relative to the moderate-stable pattern (FBG ranged from 4.9 to 5.1 mmol/L), adjusted hazard ratios (HRs) were 1.53 (95% CI 1.04, 2.26) for the elevated-stable pattern (FBG ranged from 6.1 to 6.3 mmol/L) and HR 0.61 (95% CI 0.38, 0.98) for the elevated-decreasing pattern (FBG decreased from 6.0 to 5.4 mmol/L), after adjustment for potential confounders such as age, sex, lifestyle factors, obesity, medical history, blood pressure, blood lipids, and C-reactive protein. Consistently, cumulative average and increasing rate of FBG during 2006-2010, but not a single baseline FBG, predicted future risk of MI.
We found that discrete FBG trajectories were significantly associated with subsequent risk of MI in individuals without diabetes. These observations suggest that long-term trajectories of FBG may be important for risk prediction of MI and possibly other macrovascular diseases. |
doi_str_mv | 10.2337/dc17-0610 |
format | Article |
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This prospective cohort study included 68,297 participants without diabetes (mean age 49 years) who were free of MI, stroke, and cancer prior to or in 2010 (baseline of the current analysis). FBG concentrations were measured in 2006, 2008, and 2010. The FBG trajectories during 2006-2010, the primary exposure of the current study, were identified by latent mixture modeling. Incident MI cases were confirmed via review of medical records by cardiologists.
We identified five discrete FBG trajectories according to FBG range and changing pattern over time: elevated-stable (
= 3,877), elevated-decreasing (
= 7,060), moderate-increasing (
= 10,298), moderate-stable (
= 40,352), and low-stable (
= 6,710). During 4 years of follow-up, we documented 283 incident MI cases. Relative to the moderate-stable pattern (FBG ranged from 4.9 to 5.1 mmol/L), adjusted hazard ratios (HRs) were 1.53 (95% CI 1.04, 2.26) for the elevated-stable pattern (FBG ranged from 6.1 to 6.3 mmol/L) and HR 0.61 (95% CI 0.38, 0.98) for the elevated-decreasing pattern (FBG decreased from 6.0 to 5.4 mmol/L), after adjustment for potential confounders such as age, sex, lifestyle factors, obesity, medical history, blood pressure, blood lipids, and C-reactive protein. Consistently, cumulative average and increasing rate of FBG during 2006-2010, but not a single baseline FBG, predicted future risk of MI.
We found that discrete FBG trajectories were significantly associated with subsequent risk of MI in individuals without diabetes. These observations suggest that long-term trajectories of FBG may be important for risk prediction of MI and possibly other macrovascular diseases.</description><identifier>ISSN: 0149-5992</identifier><identifier>EISSN: 1935-5548</identifier><identifier>DOI: 10.2337/dc17-0610</identifier><identifier>PMID: 28887409</identifier><language>eng</language><publisher>United States: American Diabetes Association</publisher><subject>Adolescent ; Adult ; Aged ; Aged, 80 and over ; Blood Glucose - metabolism ; Blood pressure ; C-reactive protein ; C-Reactive Protein - metabolism ; Cancer ; Cardiovascular and Metabolic Risk ; Cerebral infarction ; Cholesterol - blood ; Diabetes ; Diabetes mellitus ; Diabetes Mellitus - blood ; Diabetes Mellitus - diagnosis ; Fasting ; Female ; Follow-Up Studies ; Glucose ; Health risks ; Heart attacks ; Humans ; Incidence ; Laboratory testing ; Life Style ; Lipids ; Male ; Medical records ; Middle Aged ; Myocardial infarction ; Myocardial Infarction - blood ; Myocardial Infarction - epidemiology ; Obesity - blood ; Predictions ; Prospective Studies ; Research design ; Risk Factors ; Stroke - blood ; Stroke - epidemiology ; Trajectories ; Triglycerides - blood ; Young Adult</subject><ispartof>Diabetes care, 2017-11, Vol.40 (11), p.1565-1572</ispartof><rights>2017 by the American Diabetes Association.</rights><rights>Copyright American Diabetes Association Nov 2017</rights><rights>2017 by the American Diabetes Association. 2017</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c469t-528bb219a991450ee94a17773f33569fb07de967895d504f8784a8710ea8d5e93</citedby><cites>FETCH-LOGICAL-c469t-528bb219a991450ee94a17773f33569fb07de967895d504f8784a8710ea8d5e93</cites><orcidid>0000-0003-2617-6509</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,776,780,881,27901,27902</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28887409$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Jin, Cheng</creatorcontrib><creatorcontrib>Chen, Shuohua</creatorcontrib><creatorcontrib>Vaidya, Anand</creatorcontrib><creatorcontrib>Wu, Yuntao</creatorcontrib><creatorcontrib>Wu, Zhijun</creatorcontrib><creatorcontrib>Hu, Frank B</creatorcontrib><creatorcontrib>Kris-Etherton, Penny</creatorcontrib><creatorcontrib>Wu, Shouling</creatorcontrib><creatorcontrib>Gao, Xiang</creatorcontrib><title>Longitudinal Change in Fasting Blood Glucose and Myocardial Infarction Risk in a Population Without Diabetes</title><title>Diabetes care</title><addtitle>Diabetes Care</addtitle><description>To examine the change in fasting blood glucose (FBG) during repeated assessments over time and its potential impact on the risk of developing myocardial infarction (MI).
This prospective cohort study included 68,297 participants without diabetes (mean age 49 years) who were free of MI, stroke, and cancer prior to or in 2010 (baseline of the current analysis). FBG concentrations were measured in 2006, 2008, and 2010. The FBG trajectories during 2006-2010, the primary exposure of the current study, were identified by latent mixture modeling. Incident MI cases were confirmed via review of medical records by cardiologists.
We identified five discrete FBG trajectories according to FBG range and changing pattern over time: elevated-stable (
= 3,877), elevated-decreasing (
= 7,060), moderate-increasing (
= 10,298), moderate-stable (
= 40,352), and low-stable (
= 6,710). During 4 years of follow-up, we documented 283 incident MI cases. Relative to the moderate-stable pattern (FBG ranged from 4.9 to 5.1 mmol/L), adjusted hazard ratios (HRs) were 1.53 (95% CI 1.04, 2.26) for the elevated-stable pattern (FBG ranged from 6.1 to 6.3 mmol/L) and HR 0.61 (95% CI 0.38, 0.98) for the elevated-decreasing pattern (FBG decreased from 6.0 to 5.4 mmol/L), after adjustment for potential confounders such as age, sex, lifestyle factors, obesity, medical history, blood pressure, blood lipids, and C-reactive protein. Consistently, cumulative average and increasing rate of FBG during 2006-2010, but not a single baseline FBG, predicted future risk of MI.
We found that discrete FBG trajectories were significantly associated with subsequent risk of MI in individuals without diabetes. These observations suggest that long-term trajectories of FBG may be important for risk prediction of MI and possibly other macrovascular diseases.</description><subject>Adolescent</subject><subject>Adult</subject><subject>Aged</subject><subject>Aged, 80 and over</subject><subject>Blood Glucose - metabolism</subject><subject>Blood pressure</subject><subject>C-reactive protein</subject><subject>C-Reactive Protein - metabolism</subject><subject>Cancer</subject><subject>Cardiovascular and Metabolic Risk</subject><subject>Cerebral infarction</subject><subject>Cholesterol - blood</subject><subject>Diabetes</subject><subject>Diabetes mellitus</subject><subject>Diabetes Mellitus - blood</subject><subject>Diabetes Mellitus - diagnosis</subject><subject>Fasting</subject><subject>Female</subject><subject>Follow-Up Studies</subject><subject>Glucose</subject><subject>Health risks</subject><subject>Heart attacks</subject><subject>Humans</subject><subject>Incidence</subject><subject>Laboratory testing</subject><subject>Life Style</subject><subject>Lipids</subject><subject>Male</subject><subject>Medical records</subject><subject>Middle Aged</subject><subject>Myocardial infarction</subject><subject>Myocardial Infarction - blood</subject><subject>Myocardial Infarction - epidemiology</subject><subject>Obesity - blood</subject><subject>Predictions</subject><subject>Prospective Studies</subject><subject>Research design</subject><subject>Risk Factors</subject><subject>Stroke - blood</subject><subject>Stroke - epidemiology</subject><subject>Trajectories</subject><subject>Triglycerides - blood</subject><subject>Young Adult</subject><issn>0149-5992</issn><issn>1935-5548</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpdkU-LFDEQxYMo7rh68AtIwMt66DXpJJ3kIrij-wdGFFE8hupOeiZrJhmTtLDf3m53XdRTQdWvHo_3EHpOyWnLmHxtByob0lHyAK2oZqIRgquHaEUo143Quj1CT0q5JoRwrtRjdNQqpSQneoXCJsWtr5P1EQJe7yBuHfYRn0OpPm7xWUjJ4oswDak4DNHiDzdpgGz9jF_FEfJQfYr4sy_flz_An9JhCvB7-c3XXZoqfuehd9WVp-jRCKG4Z3fzGH09f_9lfdlsPl5crd9umoF3ujaiVX3fUg1aUy6Ic5oDlVKykTHR6bEn0jrdSaWFFYSPSioOSlLiQFnhNDtGb251D1O_d3ZwsWYI5pD9HvKNSeDNv5fod2abfhrRiVYoNQuc3Ank9GNypZq9L4MLAaJLUzFzyFIwQtiCvvwPvU5TnsNcKEWl7lTXzdSrW2rIqZTsxnszlJilQ7N0aJYOZ_bF3-7vyT-lsV-WKJcQ</recordid><startdate>20171101</startdate><enddate>20171101</enddate><creator>Jin, Cheng</creator><creator>Chen, Shuohua</creator><creator>Vaidya, Anand</creator><creator>Wu, Yuntao</creator><creator>Wu, Zhijun</creator><creator>Hu, Frank B</creator><creator>Kris-Etherton, Penny</creator><creator>Wu, Shouling</creator><creator>Gao, Xiang</creator><general>American Diabetes Association</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>K9.</scope><scope>NAPCQ</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0003-2617-6509</orcidid></search><sort><creationdate>20171101</creationdate><title>Longitudinal Change in Fasting Blood Glucose and Myocardial Infarction Risk in a Population Without Diabetes</title><author>Jin, Cheng ; Chen, Shuohua ; Vaidya, Anand ; Wu, Yuntao ; Wu, Zhijun ; Hu, Frank B ; Kris-Etherton, Penny ; Wu, Shouling ; Gao, Xiang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c469t-528bb219a991450ee94a17773f33569fb07de967895d504f8784a8710ea8d5e93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Adolescent</topic><topic>Adult</topic><topic>Aged</topic><topic>Aged, 80 and over</topic><topic>Blood Glucose - metabolism</topic><topic>Blood pressure</topic><topic>C-reactive protein</topic><topic>C-Reactive Protein - metabolism</topic><topic>Cancer</topic><topic>Cardiovascular and Metabolic Risk</topic><topic>Cerebral infarction</topic><topic>Cholesterol - blood</topic><topic>Diabetes</topic><topic>Diabetes mellitus</topic><topic>Diabetes Mellitus - blood</topic><topic>Diabetes Mellitus - diagnosis</topic><topic>Fasting</topic><topic>Female</topic><topic>Follow-Up Studies</topic><topic>Glucose</topic><topic>Health risks</topic><topic>Heart attacks</topic><topic>Humans</topic><topic>Incidence</topic><topic>Laboratory testing</topic><topic>Life Style</topic><topic>Lipids</topic><topic>Male</topic><topic>Medical records</topic><topic>Middle Aged</topic><topic>Myocardial infarction</topic><topic>Myocardial Infarction - blood</topic><topic>Myocardial Infarction - epidemiology</topic><topic>Obesity - blood</topic><topic>Predictions</topic><topic>Prospective Studies</topic><topic>Research design</topic><topic>Risk Factors</topic><topic>Stroke - blood</topic><topic>Stroke - epidemiology</topic><topic>Trajectories</topic><topic>Triglycerides - blood</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jin, Cheng</creatorcontrib><creatorcontrib>Chen, Shuohua</creatorcontrib><creatorcontrib>Vaidya, Anand</creatorcontrib><creatorcontrib>Wu, Yuntao</creatorcontrib><creatorcontrib>Wu, Zhijun</creatorcontrib><creatorcontrib>Hu, Frank B</creatorcontrib><creatorcontrib>Kris-Etherton, Penny</creatorcontrib><creatorcontrib>Wu, Shouling</creatorcontrib><creatorcontrib>Gao, Xiang</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Nursing & Allied Health Premium</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Diabetes care</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jin, Cheng</au><au>Chen, Shuohua</au><au>Vaidya, Anand</au><au>Wu, Yuntao</au><au>Wu, Zhijun</au><au>Hu, Frank B</au><au>Kris-Etherton, Penny</au><au>Wu, Shouling</au><au>Gao, Xiang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Longitudinal Change in Fasting Blood Glucose and Myocardial Infarction Risk in a Population Without Diabetes</atitle><jtitle>Diabetes care</jtitle><addtitle>Diabetes Care</addtitle><date>2017-11-01</date><risdate>2017</risdate><volume>40</volume><issue>11</issue><spage>1565</spage><epage>1572</epage><pages>1565-1572</pages><issn>0149-5992</issn><eissn>1935-5548</eissn><abstract>To examine the change in fasting blood glucose (FBG) during repeated assessments over time and its potential impact on the risk of developing myocardial infarction (MI).
This prospective cohort study included 68,297 participants without diabetes (mean age 49 years) who were free of MI, stroke, and cancer prior to or in 2010 (baseline of the current analysis). FBG concentrations were measured in 2006, 2008, and 2010. The FBG trajectories during 2006-2010, the primary exposure of the current study, were identified by latent mixture modeling. Incident MI cases were confirmed via review of medical records by cardiologists.
We identified five discrete FBG trajectories according to FBG range and changing pattern over time: elevated-stable (
= 3,877), elevated-decreasing (
= 7,060), moderate-increasing (
= 10,298), moderate-stable (
= 40,352), and low-stable (
= 6,710). During 4 years of follow-up, we documented 283 incident MI cases. Relative to the moderate-stable pattern (FBG ranged from 4.9 to 5.1 mmol/L), adjusted hazard ratios (HRs) were 1.53 (95% CI 1.04, 2.26) for the elevated-stable pattern (FBG ranged from 6.1 to 6.3 mmol/L) and HR 0.61 (95% CI 0.38, 0.98) for the elevated-decreasing pattern (FBG decreased from 6.0 to 5.4 mmol/L), after adjustment for potential confounders such as age, sex, lifestyle factors, obesity, medical history, blood pressure, blood lipids, and C-reactive protein. Consistently, cumulative average and increasing rate of FBG during 2006-2010, but not a single baseline FBG, predicted future risk of MI.
We found that discrete FBG trajectories were significantly associated with subsequent risk of MI in individuals without diabetes. These observations suggest that long-term trajectories of FBG may be important for risk prediction of MI and possibly other macrovascular diseases.</abstract><cop>United States</cop><pub>American Diabetes Association</pub><pmid>28887409</pmid><doi>10.2337/dc17-0610</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0003-2617-6509</orcidid><oa>free_for_read</oa></addata></record> |
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source | MEDLINE; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals |
subjects | Adolescent Adult Aged Aged, 80 and over Blood Glucose - metabolism Blood pressure C-reactive protein C-Reactive Protein - metabolism Cancer Cardiovascular and Metabolic Risk Cerebral infarction Cholesterol - blood Diabetes Diabetes mellitus Diabetes Mellitus - blood Diabetes Mellitus - diagnosis Fasting Female Follow-Up Studies Glucose Health risks Heart attacks Humans Incidence Laboratory testing Life Style Lipids Male Medical records Middle Aged Myocardial infarction Myocardial Infarction - blood Myocardial Infarction - epidemiology Obesity - blood Predictions Prospective Studies Research design Risk Factors Stroke - blood Stroke - epidemiology Trajectories Triglycerides - blood Young Adult |
title | Longitudinal Change in Fasting Blood Glucose and Myocardial Infarction Risk in a Population Without Diabetes |
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