Relationship between Lipid Profiles and Glycemic Control Among Patients with Type 2 Diabetes in Qingdao, China
Glycosylated hemoglobin (HbA1c) was the best indicator of glycemic control, which did not show the dynamic relationship between glycemic control and lipid profiles. In order to guide the health management of Type 2 diabetes (T2D), we assessed the levels of lipid profiles and fasting plasma glucose (...
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description | Glycosylated hemoglobin (HbA1c) was the best indicator of glycemic control, which did not show the dynamic relationship between glycemic control and lipid profiles. In order to guide the health management of Type 2 diabetes (T2D), we assessed the levels of lipid profiles and fasting plasma glucose (FPG) and displayed the relationship between FPG control and lipid profiles. We conducted a cross-sectional study that included 5822 participants. Descriptive statistics were conducted according to gender and glycemic status respectively. Comparisons for the control of lipid profiles were conducted according to glycemic control. Four logistic regression models were generated to analyze the relationship between lipid profiles and glycemic control according to different confounding factors. The metabolic control percentage of FPG, triglyceride (TG), total cholesterol (TC), high density lipoprotein cholesterol (HDL-C) and low density lipoprotein cholesterol (LDL-C) was 27.50%, 73.10%, 28.10%, 64.20% and 44.80% respectively. In the fourth model with the most confounding factors, the odds ratios (ORs) and 95% confidence intervals (CIs) of TG, TC, LDL-C and HDL-C were 0.989 (0.935, 1.046), 0.862 (0.823, 0.903), 0.987 (0.920, 1.060) and 2.173 (1.761, 2.683). TC and HDL-C were statistically significant, and TG and LDL-C were not statistically significant with adjustment for different confounding factors. In conclusion, FPG was significantly associated with HDL and TC and was not associated with LDL and TG. Our findings suggested that TC and HDL should be focused on in the process of T2D health management. |
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In order to guide the health management of Type 2 diabetes (T2D), we assessed the levels of lipid profiles and fasting plasma glucose (FPG) and displayed the relationship between FPG control and lipid profiles. We conducted a cross-sectional study that included 5822 participants. Descriptive statistics were conducted according to gender and glycemic status respectively. Comparisons for the control of lipid profiles were conducted according to glycemic control. Four logistic regression models were generated to analyze the relationship between lipid profiles and glycemic control according to different confounding factors. The metabolic control percentage of FPG, triglyceride (TG), total cholesterol (TC), high density lipoprotein cholesterol (HDL-C) and low density lipoprotein cholesterol (LDL-C) was 27.50%, 73.10%, 28.10%, 64.20% and 44.80% respectively. In the fourth model with the most confounding factors, the odds ratios (ORs) and 95% confidence intervals (CIs) of TG, TC, LDL-C and HDL-C were 0.989 (0.935, 1.046), 0.862 (0.823, 0.903), 0.987 (0.920, 1.060) and 2.173 (1.761, 2.683). TC and HDL-C were statistically significant, and TG and LDL-C were not statistically significant with adjustment for different confounding factors. In conclusion, FPG was significantly associated with HDL and TC and was not associated with LDL and TG. Our findings suggested that TC and HDL should be focused on in the process of T2D health management.</description><identifier>ISSN: 1660-4601</identifier><identifier>ISSN: 1661-7827</identifier><identifier>EISSN: 1660-4601</identifier><identifier>DOI: 10.3390/ijerph17155317</identifier><identifier>PMID: 32718055</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Age ; Alcohol ; Body mass index ; Cardiovascular disease ; Cholesterol ; Confidence intervals ; Cross-sectional studies ; Diabetes ; Diabetes mellitus ; Diabetes mellitus (non-insulin dependent) ; Diabetic retinopathy ; Exercise ; Gender ; Glucose ; Hemoglobin ; High density lipoprotein ; Hypertension ; Kinases ; Laboratories ; Lipids ; Lipoproteins ; Low density lipoprotein ; Marital status ; Plasma ; Regression analysis ; Statistical analysis ; Statistical significance ; Studies ; Triglycerides</subject><ispartof>International journal of environmental research and public health, 2020-07, Vol.17 (15), p.5317</ispartof><rights>2020. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2020 by the authors. 2020</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c395t-c34d339816a7e835532fb75c876ee3418d1c4ca45a38825456863f0a4c30d8b13</citedby><cites>FETCH-LOGICAL-c395t-c34d339816a7e835532fb75c876ee3418d1c4ca45a38825456863f0a4c30d8b13</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7432328/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC7432328/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,315,728,781,785,886,27926,27927,53793,53795</link.rule.ids></links><search><creatorcontrib>Wang, Shukang</creatorcontrib><creatorcontrib>Ji, Xiaokang</creatorcontrib><creatorcontrib>Zhang, Zhentang</creatorcontrib><creatorcontrib>Xue, Fuzhong</creatorcontrib><title>Relationship between Lipid Profiles and Glycemic Control Among Patients with Type 2 Diabetes in Qingdao, China</title><title>International journal of environmental research and public health</title><description>Glycosylated hemoglobin (HbA1c) was the best indicator of glycemic control, which did not show the dynamic relationship between glycemic control and lipid profiles. In order to guide the health management of Type 2 diabetes (T2D), we assessed the levels of lipid profiles and fasting plasma glucose (FPG) and displayed the relationship between FPG control and lipid profiles. We conducted a cross-sectional study that included 5822 participants. Descriptive statistics were conducted according to gender and glycemic status respectively. Comparisons for the control of lipid profiles were conducted according to glycemic control. Four logistic regression models were generated to analyze the relationship between lipid profiles and glycemic control according to different confounding factors. The metabolic control percentage of FPG, triglyceride (TG), total cholesterol (TC), high density lipoprotein cholesterol (HDL-C) and low density lipoprotein cholesterol (LDL-C) was 27.50%, 73.10%, 28.10%, 64.20% and 44.80% respectively. In the fourth model with the most confounding factors, the odds ratios (ORs) and 95% confidence intervals (CIs) of TG, TC, LDL-C and HDL-C were 0.989 (0.935, 1.046), 0.862 (0.823, 0.903), 0.987 (0.920, 1.060) and 2.173 (1.761, 2.683). TC and HDL-C were statistically significant, and TG and LDL-C were not statistically significant with adjustment for different confounding factors. In conclusion, FPG was significantly associated with HDL and TC and was not associated with LDL and TG. Our findings suggested that TC and HDL should be focused on in the process of T2D health management.</description><subject>Age</subject><subject>Alcohol</subject><subject>Body mass index</subject><subject>Cardiovascular disease</subject><subject>Cholesterol</subject><subject>Confidence intervals</subject><subject>Cross-sectional studies</subject><subject>Diabetes</subject><subject>Diabetes mellitus</subject><subject>Diabetes mellitus (non-insulin dependent)</subject><subject>Diabetic retinopathy</subject><subject>Exercise</subject><subject>Gender</subject><subject>Glucose</subject><subject>Hemoglobin</subject><subject>High density lipoprotein</subject><subject>Hypertension</subject><subject>Kinases</subject><subject>Laboratories</subject><subject>Lipids</subject><subject>Lipoproteins</subject><subject>Low density lipoprotein</subject><subject>Marital status</subject><subject>Plasma</subject><subject>Regression analysis</subject><subject>Statistical analysis</subject><subject>Statistical significance</subject><subject>Studies</subject><subject>Triglycerides</subject><issn>1660-4601</issn><issn>1661-7827</issn><issn>1660-4601</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNpdkc1rGzEQxUVoyfc1Z0EvPdSJviVfAsFt04KhSUjOQtbOemV2pY20bvB_X4WE0OQyMzC_ebzHIHRGyTnnc3IRNpDHjmoqJad6Dx1SpchMKEI__TcfoKNSNoRwI9R8Hx1wpqkhUh6ieAe9m0KKpQsjXsH0BBDxMoyhwTc5taGHgl1s8HW_8zAEjxcpTjn1-GpIcY1v6jHEqeCnMHX4fjcCZvh7cFWpHoaIb0NcNy59w4suRHeCPreuL3D62o_Rw88f94tfs-Wf69-Lq-XM87mcahVNjWeochoMr9lYu9LSG60AuKCmoV54J6TjxjAppDKKt8QJz0ljVpQfo8sX3XG7GqDx1WJ2vR1zGFze2eSCfb-JobPr9NdqwRlnpgp8fRXI6XELZbJDKB763kVI22KZYIYoqvW8ol8-oJu0zbHGe6a0lIYJVanzF8rnVEqG9s0MJfb5lfb9K_k_sKOQ6w</recordid><startdate>20200723</startdate><enddate>20200723</enddate><creator>Wang, Shukang</creator><creator>Ji, Xiaokang</creator><creator>Zhang, Zhentang</creator><creator>Xue, Fuzhong</creator><general>MDPI AG</general><general>MDPI</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8C1</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>M1P</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>20200723</creationdate><title>Relationship between Lipid Profiles and Glycemic Control Among Patients with Type 2 Diabetes in Qingdao, China</title><author>Wang, Shukang ; Ji, Xiaokang ; Zhang, Zhentang ; Xue, Fuzhong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c395t-c34d339816a7e835532fb75c876ee3418d1c4ca45a38825456863f0a4c30d8b13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Age</topic><topic>Alcohol</topic><topic>Body mass index</topic><topic>Cardiovascular disease</topic><topic>Cholesterol</topic><topic>Confidence intervals</topic><topic>Cross-sectional studies</topic><topic>Diabetes</topic><topic>Diabetes mellitus</topic><topic>Diabetes mellitus (non-insulin dependent)</topic><topic>Diabetic retinopathy</topic><topic>Exercise</topic><topic>Gender</topic><topic>Glucose</topic><topic>Hemoglobin</topic><topic>High density lipoprotein</topic><topic>Hypertension</topic><topic>Kinases</topic><topic>Laboratories</topic><topic>Lipids</topic><topic>Lipoproteins</topic><topic>Low density lipoprotein</topic><topic>Marital status</topic><topic>Plasma</topic><topic>Regression analysis</topic><topic>Statistical analysis</topic><topic>Statistical significance</topic><topic>Studies</topic><topic>Triglycerides</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Wang, Shukang</creatorcontrib><creatorcontrib>Ji, Xiaokang</creatorcontrib><creatorcontrib>Zhang, Zhentang</creatorcontrib><creatorcontrib>Xue, Fuzhong</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Public Health Database</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Medical Database</collection><collection>Access via ProQuest (Open Access)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>International journal of environmental research and public health</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Shukang</au><au>Ji, Xiaokang</au><au>Zhang, Zhentang</au><au>Xue, Fuzhong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Relationship between Lipid Profiles and Glycemic Control Among Patients with Type 2 Diabetes in Qingdao, China</atitle><jtitle>International journal of environmental research and public health</jtitle><date>2020-07-23</date><risdate>2020</risdate><volume>17</volume><issue>15</issue><spage>5317</spage><pages>5317-</pages><issn>1660-4601</issn><issn>1661-7827</issn><eissn>1660-4601</eissn><abstract>Glycosylated hemoglobin (HbA1c) was the best indicator of glycemic control, which did not show the dynamic relationship between glycemic control and lipid profiles. In order to guide the health management of Type 2 diabetes (T2D), we assessed the levels of lipid profiles and fasting plasma glucose (FPG) and displayed the relationship between FPG control and lipid profiles. We conducted a cross-sectional study that included 5822 participants. Descriptive statistics were conducted according to gender and glycemic status respectively. Comparisons for the control of lipid profiles were conducted according to glycemic control. Four logistic regression models were generated to analyze the relationship between lipid profiles and glycemic control according to different confounding factors. The metabolic control percentage of FPG, triglyceride (TG), total cholesterol (TC), high density lipoprotein cholesterol (HDL-C) and low density lipoprotein cholesterol (LDL-C) was 27.50%, 73.10%, 28.10%, 64.20% and 44.80% respectively. In the fourth model with the most confounding factors, the odds ratios (ORs) and 95% confidence intervals (CIs) of TG, TC, LDL-C and HDL-C were 0.989 (0.935, 1.046), 0.862 (0.823, 0.903), 0.987 (0.920, 1.060) and 2.173 (1.761, 2.683). TC and HDL-C were statistically significant, and TG and LDL-C were not statistically significant with adjustment for different confounding factors. In conclusion, FPG was significantly associated with HDL and TC and was not associated with LDL and TG. Our findings suggested that TC and HDL should be focused on in the process of T2D health management.</abstract><cop>Basel</cop><pub>MDPI AG</pub><pmid>32718055</pmid><doi>10.3390/ijerph17155317</doi><oa>free_for_read</oa></addata></record> |
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subjects | Age Alcohol Body mass index Cardiovascular disease Cholesterol Confidence intervals Cross-sectional studies Diabetes Diabetes mellitus Diabetes mellitus (non-insulin dependent) Diabetic retinopathy Exercise Gender Glucose Hemoglobin High density lipoprotein Hypertension Kinases Laboratories Lipids Lipoproteins Low density lipoprotein Marital status Plasma Regression analysis Statistical analysis Statistical significance Studies Triglycerides |
title | Relationship between Lipid Profiles and Glycemic Control Among Patients with Type 2 Diabetes in Qingdao, China |
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