Early postpartum dyslipidemia and its potential predictors during pregnancy in women with a history of gestational diabetes mellitus
Background This study aimed to analyze the incidence of early postpartum dyslipidemia and its potential predictors in women with a history of gestational diabetes mellitus (GDM). Methods This was a retrospective study. Five hundred eighty-nine women diagnosed with GDM were enrolled and followed up a...
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Published in | Lipids in health and disease Vol. 19; no. 1; pp. 1 - 8 |
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Abstract | Background This study aimed to analyze the incidence of early postpartum dyslipidemia and its potential predictors in women with a history of gestational diabetes mellitus (GDM). Methods This was a retrospective study. Five hundred eighty-nine women diagnosed with GDM were enrolled and followed up at 6-12 weeks after delivery. A 75 g oral glucose tolerance test (OGTT) and lipid levels were performed during mid-trimester and the early postpartum period. Participants were divided into the normal lipid group and dyslipidemia group according to postpartum lipid levels. Demographic and metabolic parameters were analyzed. Multiple logistic regression was performed to analyze the potential predictors for early postpartum dyslipidemia. A receiver operating characteristic curve (ROC) was calculated to determine the cut-off values. Results A total of 38.5% of the 589 women developed dyslipidemia in early postpartum and 60% of them had normal glucose metabolism. Delivery age, systolic blood pressure (SBP), glycated hemoglobin (HbA1c) and low-density lipoprotein cholesterol (LDL-C) were independent predictors of early postpartum dyslipidemia in women with a history of GDM. The cut-offs of maternal age, SBP, HbA1c values, and LDL-C levels were 35 years, 123 mmHg, 5.1%, and 3.56 mmol/L, respectively. LDL-C achieved a balanced mix of high sensitivity (63.9%) and specificity (69.2%), with the highest area under the receiver operating characteristic curve (AUC) (0.696). When LDL-C was combined with age, SBP, and HbA1c, the AUC reached to 0.733. Conclusions A lipid metabolism evaluation should be recommended in women with a history of GDM after delivery, particularly those with a maternal age > 35 years, SBP > 123 mmHg before labor, HbA1c value > 5.1%, or LDL-C levels > 3.56 mmol/L in the second trimester of pregnancy. Keywords: Gestational diabetes mellitus, Predictor, Lipid, postpartum, Cardiovascular disease |
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AbstractList | Abstract Background This study aimed to analyze the incidence of early postpartum dyslipidemia and its potential predictors in women with a history of gestational diabetes mellitus (GDM). Methods This was a retrospective study. Five hundred eighty-nine women diagnosed with GDM were enrolled and followed up at 6–12 weeks after delivery. A 75 g oral glucose tolerance test (OGTT) and lipid levels were performed during mid-trimester and the early postpartum period. Participants were divided into the normal lipid group and dyslipidemia group according to postpartum lipid levels. Demographic and metabolic parameters were analyzed. Multiple logistic regression was performed to analyze the potential predictors for early postpartum dyslipidemia. A receiver operating characteristic curve (ROC) was calculated to determine the cut-off values. Results A total of 38.5% of the 589 women developed dyslipidemia in early postpartum and 60% of them had normal glucose metabolism. Delivery age, systolic blood pressure (SBP), glycated hemoglobin (HbA1c) and low-density lipoprotein cholesterol (LDL-C) were independent predictors of early postpartum dyslipidemia in women with a history of GDM. The cut-offs of maternal age, SBP, HbA1c values, and LDL-C levels were 35 years, 123 mmHg, 5.1%, and 3.56 mmol/L, respectively. LDL-C achieved a balanced mix of high sensitivity (63.9%) and specificity (69.2%), with the highest area under the receiver operating characteristic curve (AUC) (0.696). When LDL-C was combined with age, SBP, and HbA1c, the AUC reached to 0.733. Conclusions A lipid metabolism evaluation should be recommended in women with a history of GDM after delivery, particularly those with a maternal age > 35 years, SBP > 123 mmHg before labor, HbA1c value > 5.1%, or LDL-C levels > 3.56 mmol/L in the second trimester of pregnancy. Background This study aimed to analyze the incidence of early postpartum dyslipidemia and its potential predictors in women with a history of gestational diabetes mellitus (GDM). Methods This was a retrospective study. Five hundred eighty-nine women diagnosed with GDM were enrolled and followed up at 6-12 weeks after delivery. A 75 g oral glucose tolerance test (OGTT) and lipid levels were performed during mid-trimester and the early postpartum period. Participants were divided into the normal lipid group and dyslipidemia group according to postpartum lipid levels. Demographic and metabolic parameters were analyzed. Multiple logistic regression was performed to analyze the potential predictors for early postpartum dyslipidemia. A receiver operating characteristic curve (ROC) was calculated to determine the cut-off values. Results A total of 38.5% of the 589 women developed dyslipidemia in early postpartum and 60% of them had normal glucose metabolism. Delivery age, systolic blood pressure (SBP), glycated hemoglobin (HbA1c) and low-density lipoprotein cholesterol (LDL-C) were independent predictors of early postpartum dyslipidemia in women with a history of GDM. The cut-offs of maternal age, SBP, HbA1c values, and LDL-C levels were 35 years, 123 mmHg, 5.1%, and 3.56 mmol/L, respectively. LDL-C achieved a balanced mix of high sensitivity (63.9%) and specificity (69.2%), with the highest area under the receiver operating characteristic curve (AUC) (0.696). When LDL-C was combined with age, SBP, and HbA1c, the AUC reached to 0.733. Conclusions A lipid metabolism evaluation should be recommended in women with a history of GDM after delivery, particularly those with a maternal age > 35 years, SBP > 123 mmHg before labor, HbA1c value > 5.1%, or LDL-C levels > 3.56 mmol/L in the second trimester of pregnancy. Keywords: Gestational diabetes mellitus, Predictor, Lipid, postpartum, Cardiovascular disease This study aimed to analyze the incidence of early postpartum dyslipidemia and its potential predictors in women with a history of gestational diabetes mellitus (GDM).BACKGROUNDThis study aimed to analyze the incidence of early postpartum dyslipidemia and its potential predictors in women with a history of gestational diabetes mellitus (GDM).This was a retrospective study. Five hundred eighty-nine women diagnosed with GDM were enrolled and followed up at 6-12 weeks after delivery. A 75 g oral glucose tolerance test (OGTT) and lipid levels were performed during mid-trimester and the early postpartum period. Participants were divided into the normal lipid group and dyslipidemia group according to postpartum lipid levels. Demographic and metabolic parameters were analyzed. Multiple logistic regression was performed to analyze the potential predictors for early postpartum dyslipidemia. A receiver operating characteristic curve (ROC) was calculated to determine the cut-off values.METHODSThis was a retrospective study. Five hundred eighty-nine women diagnosed with GDM were enrolled and followed up at 6-12 weeks after delivery. A 75 g oral glucose tolerance test (OGTT) and lipid levels were performed during mid-trimester and the early postpartum period. Participants were divided into the normal lipid group and dyslipidemia group according to postpartum lipid levels. Demographic and metabolic parameters were analyzed. Multiple logistic regression was performed to analyze the potential predictors for early postpartum dyslipidemia. A receiver operating characteristic curve (ROC) was calculated to determine the cut-off values.A total of 38.5% of the 589 women developed dyslipidemia in early postpartum and 60% of them had normal glucose metabolism. Delivery age, systolic blood pressure (SBP), glycated hemoglobin (HbA1c) and low-density lipoprotein cholesterol (LDL-C) were independent predictors of early postpartum dyslipidemia in women with a history of GDM. The cut-offs of maternal age, SBP, HbA1c values, and LDL-C levels were 35 years, 123 mmHg, 5.1%, and 3.56 mmol/L, respectively. LDL-C achieved a balanced mix of high sensitivity (63.9%) and specificity (69.2%), with the highest area under the receiver operating characteristic curve (AUC) (0.696). When LDL-C was combined with age, SBP, and HbA1c, the AUC reached to 0.733.RESULTSA total of 38.5% of the 589 women developed dyslipidemia in early postpartum and 60% of them had normal glucose metabolism. Delivery age, systolic blood pressure (SBP), glycated hemoglobin (HbA1c) and low-density lipoprotein cholesterol (LDL-C) were independent predictors of early postpartum dyslipidemia in women with a history of GDM. The cut-offs of maternal age, SBP, HbA1c values, and LDL-C levels were 35 years, 123 mmHg, 5.1%, and 3.56 mmol/L, respectively. LDL-C achieved a balanced mix of high sensitivity (63.9%) and specificity (69.2%), with the highest area under the receiver operating characteristic curve (AUC) (0.696). When LDL-C was combined with age, SBP, and HbA1c, the AUC reached to 0.733.A lipid metabolism evaluation should be recommended in women with a history of GDM after delivery, particularly those with a maternal age > 35 years, SBP > 123 mmHg before labor, HbA1c value > 5.1%, or LDL-C levels > 3.56 mmol/L in the second trimester of pregnancy.CONCLUSIONSA lipid metabolism evaluation should be recommended in women with a history of GDM after delivery, particularly those with a maternal age > 35 years, SBP > 123 mmHg before labor, HbA1c value > 5.1%, or LDL-C levels > 3.56 mmol/L in the second trimester of pregnancy. This study aimed to analyze the incidence of early postpartum dyslipidemia and its potential predictors in women with a history of gestational diabetes mellitus (GDM). This was a retrospective study. Five hundred eighty-nine women diagnosed with GDM were enrolled and followed up at 6-12 weeks after delivery. A 75 g oral glucose tolerance test (OGTT) and lipid levels were performed during mid-trimester and the early postpartum period. Participants were divided into the normal lipid group and dyslipidemia group according to postpartum lipid levels. Demographic and metabolic parameters were analyzed. Multiple logistic regression was performed to analyze the potential predictors for early postpartum dyslipidemia. A receiver operating characteristic curve (ROC) was calculated to determine the cut-off values. A total of 38.5% of the 589 women developed dyslipidemia in early postpartum and 60% of them had normal glucose metabolism. Delivery age, systolic blood pressure (SBP), glycated hemoglobin (HbA1c) and low-density lipoprotein cholesterol (LDL-C) were independent predictors of early postpartum dyslipidemia in women with a history of GDM. The cut-offs of maternal age, SBP, HbA1c values, and LDL-C levels were 35 years, 123 mmHg, 5.1%, and 3.56 mmol/L, respectively. LDL-C achieved a balanced mix of high sensitivity (63.9%) and specificity (69.2%), with the highest area under the receiver operating characteristic curve (AUC) (0.696). When LDL-C was combined with age, SBP, and HbA1c, the AUC reached to 0.733. A lipid metabolism evaluation should be recommended in women with a history of GDM after delivery, particularly those with a maternal age > 35 years, SBP > 123 mmHg before labor, HbA1c value > 5.1%, or LDL-C levels > 3.56 mmol/L in the second trimester of pregnancy. Background This study aimed to analyze the incidence of early postpartum dyslipidemia and its potential predictors in women with a history of gestational diabetes mellitus (GDM). Methods This was a retrospective study. Five hundred eighty-nine women diagnosed with GDM were enrolled and followed up at 6–12 weeks after delivery. A 75 g oral glucose tolerance test (OGTT) and lipid levels were performed during mid-trimester and the early postpartum period. Participants were divided into the normal lipid group and dyslipidemia group according to postpartum lipid levels. Demographic and metabolic parameters were analyzed. Multiple logistic regression was performed to analyze the potential predictors for early postpartum dyslipidemia. A receiver operating characteristic curve (ROC) was calculated to determine the cut-off values. Results A total of 38.5% of the 589 women developed dyslipidemia in early postpartum and 60% of them had normal glucose metabolism. Delivery age, systolic blood pressure (SBP), glycated hemoglobin (HbA1c) and low-density lipoprotein cholesterol (LDL-C) were independent predictors of early postpartum dyslipidemia in women with a history of GDM. The cut-offs of maternal age, SBP, HbA1c values, and LDL-C levels were 35 years, 123 mmHg, 5.1%, and 3.56 mmol/L, respectively. LDL-C achieved a balanced mix of high sensitivity (63.9%) and specificity (69.2%), with the highest area under the receiver operating characteristic curve (AUC) (0.696). When LDL-C was combined with age, SBP, and HbA1c, the AUC reached to 0.733. Conclusions A lipid metabolism evaluation should be recommended in women with a history of GDM after delivery, particularly those with a maternal age > 35 years, SBP > 123 mmHg before labor, HbA1c value > 5.1%, or LDL-C levels > 3.56 mmol/L in the second trimester of pregnancy. |
ArticleNumber | 220 |
Audience | Academic |
Author | Yue, Shufan Cao, Xiaopei Li, Zeting Pei, Ling Li, Zhuyu Chen, Haitian Xiao, Huangmeng Lai, Fenghua Li, Yanbing |
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Cites_doi | 10.2337/dc08-0706 10.1007/s00592-017-1099-2 10.1016/j.plefa.2016.10.001 10.1136/bmjdrc-2017-000445 10.1161/CIRCULATIONAHA.115.008728 10.1016/j.diabres.2013.10.012 10.1001/jama.285.19.2486 10.1016/j.cjca.2016.07.510 10.3390/nu10070839 10.1161/CIRCULATIONAHA.115.015293 10.1016/j.placenta.2018.05.011 10.1136/heartjnl-2013-303945 10.1002/(SICI)1096-9136(199807)15:7<539::AID-DIA668>3.0.CO;2-S 10.1038/s41572-019-0098-8 10.1007/s00125-019-4840-2 10.1093/eurheartj/ehw106 10.7326/0003-4819-150-4-200902170-00005 10.1111/jdi.12854 10.1016/j.plefa.2020.102098 10.1111/obr.12645 10.1016/j.jogc.2019.03.008 10.1002/lipd.12040 10.1210/jc.2010-0361 10.1136/bmjdrc-2019-000870 10.1016/j.diabres.2015.10.004 10.1016/j.jacl.2019.10.002 10.1016/j.atherosclerosis.2019.08.014 10.1016/S0140-6736(12)62027-3 10.1161/HYPERTENSIONAHA.114.04850 10.1016/j.yjmcc.2012.08.023 10.1371/journal.pone.0087863 10.1038/ejcn.2016.171 10.1016/S0140-6736(09)60731-5 10.1186/s12889-019-7827-5 10.1161/CIRCULATIONAHA.115.018352 10.1080/09513590.2018.1512094 10.1097/MOL.0b013e328304b670 10.1136/bmjdrc-2016-000250 10.1210/en.2009-0252 10.1111/jdi.13039 10.2337/db15-1383 10.1007/s11845-016-1474-y |
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References | R Kessous (1398_CR17) 2013; 99 JA Bernstein (1398_CR31) 2017; 5 G Chodick (1398_CR14) 2020; 8 B Yan (1398_CR6) 2019; 10 AC O'Higgins (1398_CR13) 2017; 186 R Retnakaran (1398_CR15) 2010; 95 MJ Pletcher (1398_CR27) 2009; 150 F Mach (1398_CR38) 2019; 290 G López Stewart (1398_CR22) 2014; 103 BR Shah (1398_CR19) 2008; 31 MC DeRuiter (1398_CR43) 2008; 19 A Khaire (1398_CR41) 2020; 157 B Mihaylova (1398_CR28) 2012; 380 S Opoku (1398_CR35) 2019; 19 Y Ma (1398_CR40) 2019; 35 JA Bernstein (1398_CR30) 2016; 4 TJ Anderson (1398_CR37) 2016; 32 C Song (1398_CR11) 2018; 19 KA Wilmot (1398_CR2) 2015; 132 KG Alberti (1398_CR24) 1998; 15 Y Wu (1398_CR1) 2016; 133 C Chee (1398_CR36) 2016; 65 M Dong (1398_CR42) 2013; 55 EG Nabel (1398_CR4) 2015; 132 L Bellamy (1398_CR10) 2009; 373 Y Xu (1398_CR16) 2014; 9 JH Veerbeek (1398_CR33) 2015; 65 HD McIntyre (1398_CR7) 2019; 5 H Berger (1398_CR21) 2019; 41 MF Piepoli (1398_CR29) 2016; 37 A Herrera Martínez (1398_CR26) 2018; 35 F Echeverría (1398_CR45) 2016; 114 C Gao (1398_CR5) 2019; 10 O Ajala (1398_CR12) 2015; 110 CK Kramer (1398_CR20) 2019; 62 C Barrera (1398_CR8) 2018; 10 M Prados (1398_CR25) 2018; 53 S McKenzie-Sampson (1398_CR18) 2018; 55 Expert Panel on Detection E (1398_CR23) 2001; 285 DK Arnett (1398_CR3) 2019; 140 Y Wang (1398_CR9) 2017; 71 F Delhaes (1398_CR44) 2018; 69 S Rütti (1398_CR39) 2009; 150 C Wen (1398_CR32) 2019; 13 JR Zhu (1398_CR34) 2018; 15 |
References_xml | – volume: 31 start-page: 1668 year: 2008 ident: 1398_CR19 publication-title: Diabetes Care doi: 10.2337/dc08-0706 – volume: 55 start-page: 315 year: 2018 ident: 1398_CR18 publication-title: Acta Diabetol doi: 10.1007/s00592-017-1099-2 – volume: 114 start-page: 28 year: 2016 ident: 1398_CR45 publication-title: Prostaglandins Leukot Essent Fat Acids doi: 10.1016/j.plefa.2016.10.001 – volume: 5 start-page: e000445 year: 2017 ident: 1398_CR31 publication-title: BMJ Open Diabetes Res Care doi: 10.1136/bmjdrc-2017-000445 – volume: 133 start-page: 2545 year: 2016 ident: 1398_CR1 publication-title: Circulation doi: 10.1161/CIRCULATIONAHA.115.008728 – volume: 15 start-page: 1 year: 2018 ident: 1398_CR34 publication-title: J Geriatr Cardiol – volume: 103 start-page: 341 year: 2014 ident: 1398_CR22 publication-title: Diabetes Res Clin Pr doi: 10.1016/j.diabres.2013.10.012 – volume: 285 start-page: 2486 year: 2001 ident: 1398_CR23 publication-title: JAMA doi: 10.1001/jama.285.19.2486 – volume: 32 start-page: 1263 year: 2016 ident: 1398_CR37 publication-title: Can J Cardiol doi: 10.1016/j.cjca.2016.07.510 – volume: 10 start-page: 839 year: 2018 ident: 1398_CR8 publication-title: Nutrients. doi: 10.3390/nu10070839 – volume: 132 start-page: 997 year: 2015 ident: 1398_CR2 publication-title: Circulation doi: 10.1161/CIRCULATIONAHA.115.015293 – volume: 69 start-page: 118 year: 2018 ident: 1398_CR44 publication-title: Placenta. doi: 10.1016/j.placenta.2018.05.011 – volume: 99 start-page: 1118 year: 2013 ident: 1398_CR17 publication-title: Heart doi: 10.1136/heartjnl-2013-303945 – volume: 15 start-page: 539 year: 1998 ident: 1398_CR24 publication-title: Diabet Med doi: 10.1002/(SICI)1096-9136(199807)15:7<539::AID-DIA668>3.0.CO;2-S – volume: 5 start-page: 47 year: 2019 ident: 1398_CR7 publication-title: Nat Rev Dis Primers doi: 10.1038/s41572-019-0098-8 – volume: 62 start-page: 905 year: 2019 ident: 1398_CR20 publication-title: Diabetologia. doi: 10.1007/s00125-019-4840-2 – volume: 37 start-page: 2315 year: 2016 ident: 1398_CR29 publication-title: Eur Heart J doi: 10.1093/eurheartj/ehw106 – volume: 150 start-page: 243 year: 2009 ident: 1398_CR27 publication-title: Ann Intern Med doi: 10.7326/0003-4819-150-4-200902170-00005 – volume: 10 start-page: 154 year: 2019 ident: 1398_CR5 publication-title: J Diabetes Invest doi: 10.1111/jdi.12854 – volume: 157 start-page: 102098 year: 2020 ident: 1398_CR41 publication-title: Prostaglandins Leukot Essent Fat Acids doi: 10.1016/j.plefa.2020.102098 – volume: 19 start-page: 421 year: 2018 ident: 1398_CR11 publication-title: Obes Rev doi: 10.1111/obr.12645 – volume: 41 start-page: 1814 year: 2019 ident: 1398_CR21 publication-title: J Obstet Gynaecol Can doi: 10.1016/j.jogc.2019.03.008 – volume: 53 start-page: 387 year: 2018 ident: 1398_CR25 publication-title: Lipids. doi: 10.1002/lipd.12040 – volume: 95 start-page: 4345 year: 2010 ident: 1398_CR15 publication-title: J Clin Endocrinol Metab doi: 10.1210/jc.2010-0361 – volume: 8 start-page: e870 year: 2020 ident: 1398_CR14 publication-title: BMJ Open Diabetes Res Care doi: 10.1136/bmjdrc-2019-000870 – volume: 35 start-page: 698 year: 2018 ident: 1398_CR26 publication-title: Nutr Hosp – volume: 110 start-page: 309 year: 2015 ident: 1398_CR12 publication-title: Diabetes Res Clin Pr doi: 10.1016/j.diabres.2015.10.004 – volume: 13 start-page: 1008 year: 2019 ident: 1398_CR32 publication-title: J Clin Lipidol doi: 10.1016/j.jacl.2019.10.002 – volume: 290 start-page: 140 year: 2019 ident: 1398_CR38 publication-title: Atherosclerosis doi: 10.1016/j.atherosclerosis.2019.08.014 – volume: 380 start-page: 581 year: 2012 ident: 1398_CR28 publication-title: Lancet. doi: 10.1016/S0140-6736(12)62027-3 – volume: 65 start-page: 600 year: 2015 ident: 1398_CR33 publication-title: Hypertension. doi: 10.1161/HYPERTENSIONAHA.114.04850 – volume: 55 start-page: 111 year: 2013 ident: 1398_CR42 publication-title: J Mol Cell Cardiol doi: 10.1016/j.yjmcc.2012.08.023 – volume: 9 start-page: e87863 year: 2014 ident: 1398_CR16 publication-title: PLoS One doi: 10.1371/journal.pone.0087863 – volume: 71 start-page: 149 year: 2017 ident: 1398_CR9 publication-title: Eur J Clin Nutr doi: 10.1038/ejcn.2016.171 – volume: 373 start-page: 1773 year: 2009 ident: 1398_CR10 publication-title: Lancet doi: 10.1016/S0140-6736(09)60731-5 – volume: 19 start-page: 1500 year: 2019 ident: 1398_CR35 publication-title: BMC Public Health doi: 10.1186/s12889-019-7827-5 – volume: 132 start-page: 989 year: 2015 ident: 1398_CR4 publication-title: Circulation. doi: 10.1161/CIRCULATIONAHA.115.018352 – volume: 35 start-page: 174 year: 2019 ident: 1398_CR40 publication-title: Gynecol Endocrinol doi: 10.1080/09513590.2018.1512094 – volume: 19 start-page: 333 year: 2008 ident: 1398_CR43 publication-title: Curr Opin Lipidol doi: 10.1097/MOL.0b013e328304b670 – volume: 140 start-page: e596 year: 2019 ident: 1398_CR3 publication-title: Circulation. – volume: 4 start-page: e000250 year: 2016 ident: 1398_CR30 publication-title: BMJ Open Diabetes Res Care doi: 10.1136/bmjdrc-2016-000250 – volume: 150 start-page: 4521 year: 2009 ident: 1398_CR39 publication-title: Endocrinology. doi: 10.1210/en.2009-0252 – volume: 10 start-page: 1358 year: 2019 ident: 1398_CR6 publication-title: J Diabetes Invest doi: 10.1111/jdi.13039 – volume: 65 start-page: 840 year: 2016 ident: 1398_CR36 publication-title: Diabetes. doi: 10.2337/db15-1383 – volume: 186 start-page: 403 year: 2017 ident: 1398_CR13 publication-title: Irish J Med Sci doi: 10.1007/s11845-016-1474-y |
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Snippet | Background This study aimed to analyze the incidence of early postpartum dyslipidemia and its potential predictors in women with a history of gestational... This study aimed to analyze the incidence of early postpartum dyslipidemia and its potential predictors in women with a history of gestational diabetes... Abstract Background This study aimed to analyze the incidence of early postpartum dyslipidemia and its potential predictors in women with a history of... |
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SubjectTerms | Age Analysis Blood pressure Cardiovascular disease Cholesterol Diabetes mellitus Dyslipidemia Gestational diabetes Gestational diabetes mellitus Glucose Glucose metabolism Glucose tolerance Glucose tolerance test Glycosylated hemoglobin Hemoglobin Lipid metabolism Lipid, postpartum Lipids Low density lipoprotein Low density lipoproteins Meat Metabolic disorders Metabolism Mortality Nutrition research Physiological aspects Plasma Postpartum Predictor Pregnancy Pregnant women Regression analysis Statistical analysis Womens health |
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Title | Early postpartum dyslipidemia and its potential predictors during pregnancy in women with a history of gestational diabetes mellitus |
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